course work for computer science

CS50: Introduction to Computer Science

This is cs50x.

An introduction to the intellectual enterprises of computer science and the art of programming in an online course from Harvard.

What You'll Learn

This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course. 

Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will be able to:

• A broad and robust understanding of computer science and programming
• How to think algorithmically and solve programming problems efficiently
• Concepts like abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development
• Familiarity in a number of languages, including C, PHP, and JavaScript plus SQL, CSS, and HTML
• How to engage with a vibrant community of like-minded learners from all levels of experience
• How to develop and present a final programming project to your peers

Your Instructors

David J. Malan

Gordon McKay Professor of the Practice of Computer Science, Harvard John A. Paulson School of Engineering and Applied Sciences

Senior Preceptor in Computer Science, Harvard University

CS50 Recommended

"Harvard's Free coding courses are excellent. You need to take them." by Python Programmer. https://youtu.be/WwEcPcfRlD0?feature=shared

"I tried Harvard University's FREE CS50: Introduction to Computer Science course | CS50 review 2020" by Sunny Singh. https://youtu.be/DSA34lhJvw4?feature=shared 

"Learn To Code For FREE At Harvard University // CS50: Introduction To Computer Science Review" by Dorian Develops. https://youtu.be/He4jqZ2EjrE?feature=shared

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $219 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums.  Please note that this track does not offer a certificate for learners who earn a passing grade.

Have Questions?

Can I enroll in this course if I'm not a programmer? Are there any prerequisites? Learn the answers to these and more in our FAQs.

Course FAQs

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CS50: Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

Associated Schools

Harvard School of Engineering and Applied Sciences

What you'll learn.

A broad and robust understanding of computer science and programming

How to think algorithmically and solve programming problems efficiently

Concepts like abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development

Familiarity with a number of languages, including C, Python, SQL, and JavaScript plus CSS and HTML

How to engage with a vibrant community of like-minded learners from all levels of experience

How to develop and present a final programming project to your peers

Course description

This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course. 

Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.

Instructors

David J. Malan

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Stanford Online

Computer science 101.

SOE-YCSCS101

Stanford School of Engineering

CS101 is a self-paced course that teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today.

In CS101, participants play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Everything works within the browser, so there is no extra software to download or install. CS101 also provides a general background on computers today: what is a computer, what is hardware, what is software, what is the internet. Anyone who has the ability to use a web browser may be successful in this course. No previous computer science experience is required.

• The nature of computers and code, what they can and cannot do
• How computer hardware works: chips, cpu, memory, disk
• Necessary jargon: bits, bytes, megabytes, gigabytes
• How software works: what is a program, what is "running"
• How digital images work
• Computer code: loops and logic
• Big ideas: abstraction, logic, bugs
• How structured data works
• How the internet works: ip address, routing, ethernet, wi-fi
• Computer security: viruses, trojans, and passwords, oh my!
• Analog vs. digital
• Digital media, images, sounds, video, compression

Nick Parlante, Senior Lecturer, Computer Science

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10 Best Computer Science Courses to Take in 2022

Are you looking for the best introductions to computer science? I’ve ranked the top courses available online, following a robust methodology. And they're all free to audit. You can read about it below.

But if you’re in a hurry, here are my top picks. Click on one to skip to the course details:

What is Computer Science?

The definition of computer science is almost as broad as the definition of physics. So, to say that computer science is the study of computers and computing concepts is just as 'useful' as saying that physics is the study of nature and its phenomena.

Instead, I’ll tell you the main subfields of computer science that most universities include in their syllabus.

• Computer architecture and organization naïvely ponders: ‘How do I design a computer?’
• Programming steps in and questions: ‘But how will the computer understand the human?’
• Operating systems interjects: ‘Hold on, how should the human interact with the computer?’
• Data structures and algorithms chirps in: ‘After you've figured that out, how do we store and compute data efficiently?’
• Networking and communication waits politely before inquiring: ‘So that’s all cool, but how can we make computers talk to each other?’

You get the gist. I’m sure you’ve had one of these intriguing thoughts pop up in your mind before. Luckily, these are the questions that computer science tries to answer.

By studying computer science, you can become a better programmer. Just as a veterinarian is likely to understand animals better than the average pet owner, by studying computer science, you can get a better grasp of the features, abilities, and limitations of these awesome code-running machines that we call ‘computers’.

Course Ranking Methodology

I followed a three-step process to build this ranking:

First , let me introduce myself. I’m part of Class Central , the leading search engine for online courses. I ( @elham ) built this ranking in collaboration with my friend and colleague @manoel , following the same approach we used with some success in our previous rankings of the best Python courses and best machine learning courses . At this point, I’d say it’s a pretty robust method.

We started building this ranking by looking at our database of 50K+ online courses . We were interested in things like ratings, reviews, and course bookmarks. This allowed us to make an initial selection. So this phase was purely data-driven.

This tentative first step rapidly helped surface some of the best options available out there. Word of mouth is very effective in online learning. Good courses get noticed. And the very best gather a lot of attention, and raving reviews.

That said, reviews don’t always tell the whole story. In fact, some courses are so good at grabbing the spotlight early on that other excellent resources can go unnoticed. So the next step was to bring our personal knowledge of online education into the mix.

Second , we used our experience as online learners to evaluate each of our initial picks.

We both come from computer science backgrounds and are prolific online learners, having completed about 45 MOOCs between us. Additionally, Manoel has an online bachelor’s in computer science , and I am currently completing my foundation in computer science.

Manoel gathered the courses while I wrote the article you’re currently reading. Throughout this process, we bounced ideas off each other and made iterative improvements to the ranking until we were both satisfied with the end result.

Third , during our research, we came across courses that felt well-made but weren’t well-known. If we adopted a purely data-centric approach, we would have to leave those courses out of the ranking, if only because they had fewer enrollments and ratings.

But no. This ranking is deliberately opinionated and holistic. When we felt confident that a course was worth including, even when the course might not yet have quite as many reviews as some of its competitors, we went with our gut and included it.

We also spiced up the list by including a wide variety of computer science courses that will hopefully cater to the diverse range of learners, whether you’re a true beginner or someone with some foundations in computer science, or an interest in specific topics like math.

After going through this process — combining Class Central data, our experience as lifelong learners, and lots of editing — we arrived at our final ranking. So far, we’ve spent more than 10 hours building this ranking, and we intend to continue updating it in the future.

Course Ranking Statistics

Here are some aggregate stats about the ranking:

• In total, the courses in this ranking accumulated over 5 million enrollments with 2 courses having over 1 million enrollments each.
• The most popular course in the list has 3.5 million enrollments.
• All of the courses in this ranking are either entirely free, or free to audit.
• With 4 courses each, edX and Coursera are tied for the most represented provider in this ranking.
• Around 480k people are following Computer Science Courses on Class Central .

Without further ado, let’s go through the top picks.

1. CS50's Introduction to Computer Science (Harvard University)

My first pick has to be CS50's Introduction to Computer Science , offered by Harvard University on edX. Launched on edX in 2012, CS50 is the computer science course on the internet. It is famous for its splendid production quality and its yearly curriculum updates.

It provides a succinct but comprehensive overview of what computer science is all about. Whether you are a newbie who has never heard of ‘Hello World!’, or a programmer who knows a thing or two about computers, you’ll come out of this course having learned something new.

One Thing to Note

Although the course exercises come in two versions, easy and challenging, I found that even the easy exercises can be a bit tricky. If you know nothing about programming, I’d recommend you find someone to study this course with.

Fortunately, CS50 has one of the largest and most active course communities online: check their Discord .

Or if you’re looking for a shorter, more practical course, you might want to have a look at my Python ranking , which includes some gentler on-ramps into the world of programming.

The Instructor

We can't discuss CS50 without bringing up David J. Malan , the Harvard professor that teaches the course. Rarely has an instructor been so instrumental to the success of a course.

Beyond being an excellent educator, Prof. Malan is a true entertainer, with near-perfect delivery. And when you’re tackling an academic course that may take you dozens of hours to complete, having an instructor capable of capturing the learner’s attention makes a huge difference.

So if despite a sincere desire to learn, you find yourself falling asleep while taking online courses, this might just be the course for you. Prof. Malan’s energy is contagious!

What You’ll Learn

The course begins with the premise that computer science is, at its core, problem solving. It introduces you to binary, the fundamental language of computers, and explains how sequences of 1s and 0s can somehow represent text, images, videos, and even sounds.

You’ll learn that algorithms are step-by-step instructions designed to solve a problem. The most common type of algorithms you’ll deal with throughout the course are algorithms for sorting and searching , like bubble sort, merge sort, and binary search.

You may wonder, ‘What’s the point of having many different algorithms if they all do the same thing?’. This is when you’ll learn about measuring the efficiency of an algorithm with Big O notation .

The first programming language the course teaches is the beginner-friendly language Scratch. Through block-based coding, you'll use Scratch to illustrate fundamental programming concepts like functions, conditional statements, boolean expressions, loops, and variables.

Later in the course, you’ll notice that these fundamental concepts keep coming up time and again, since they can be found in pretty much every programming language that CS50 will teach you.

The course then removes your training wheels and drags you down into the depths of low-level programming languages. By “low-level”, I don’t mean “less valuable”. In computer science, low-level programming languages are languages that are close to machine code: the closer they are to machine code, the “lower” they are.

Assembly language is as close as we get to binary, and the course will briefly discuss it. But our first deep dive into traditional programming (writing lines of code instead of arranging colorful blocks like with Scratch) will be with C, a low-level programming language where you'll manage memory by hand and implement your first data structures.

You’ll learn that computers store data in sequences of locations in memory, and how computers can locate and access data with addresses and pointers. You’ll also learn about the different ways we can create and store lists of values, like arrays, linked lists, and trees.

You’ll compare the advantages and disadvantages of each data structure. For example, hash tables can be accessed in constant time, but require mitigating the risk of data collision.

You’ll then be brought back up to the surface towards “higher-level” programming, where you’ll be able to comfortably breathe as you begin working with Python, and continue jumping from topic to topic.

You’ll explore SQL, the programming language of many databases. The final weeks of the course culminate in you building and designing an interactive website with HTML, CSS, JavaScript, and a Python framework called Flask.

How You’ll Learn

The course is ten weeks long, plus an open-ended final project that might take an extra week (or more, if you want to work on something really ambitious).

The course is recorded annually on-campus at Harvard before being launched online the following Spring. While the recording is ongoing, you might be able to join via live stream with a hundred other learners, or if you live near campus, even attend in person — though the pandemic might preclude this for the foreseeable future. Otherwise, you’ll have access to on-demand recordings on edX or via Harvard OCW .

Regarding assessments, you’ll complete ten problem sets, eight labs, and a final end-of-course project that you’ll have to design and come up with yourself or with a team. You’ll be able to code and submit these via a convenient in-browser VS Code-based editor.

CS50 Lineup

A lot of people have heard about CS50’s Introduction to Computer Science, but not many realize that there are 10 other courses under the CS50 brand. A few follow-up courses worth mentioning are:

• Introduction to Artificial Intelligence with Python
• Introduction to Game Development
• Web Programming with Python and JavaScript

What’s even better: many of these courses offer a free certificate. If you’d like to know more about the CS50 courses, and how to get a free certificate, you can read Manoel's CS50 guide .

• The course instructor David J. Malan has been teaching CS50 for 15 years , first on-campus at Harvard, and on edX since 2012 .
• CS50 has been bookmarked around 30k times and has over 100 reviews on Class Central.
• Every year, CS50 organizes Puzzle Day , a friendly problem-solving competition where you’ll have the opportunity to collaborate with learners worldwide.
• CS50 is a part of both our list of most-popular courses of all time and best free courses of all time .
• David J. Malan was the founder and chairman of Diskaster, a hard drive and memory card data recovery firm. One of the exercises in the course is a nod to his previous work .
• CS50 is the longest course on this ranking, owing to its comprehensiveness.

If you're interested in this course, you can find more information about the course and how to enroll here .

2. Computational Thinking for Problem Solving (University of Pennsylvania)

My second pick would be Computational Thinking for Problem Solving from the University of Pennsylvania on Coursera.

This course focuses on the skills underlying computer science  — computational thinking.

Computational thinking is the process of breaking a problem into parts, and then coming up with a resolution method that can be carried out by a computer.

Once you’ve embraced computational thinking, you’ll be in the right mindset to tackle additional computer science courses. So you could see this course as a foundation before the foundation. That said, if your interest lies in problem solving per se rather than CS as a whole, this course should also be a great fit.

You do not need any prior experience with computer science or programming to take this course, although some basic high school mathematics would be useful.

The course covers four main topics: computational thinking, algorithms, computer architecture, and Python.

First, the course outlines the four pillars of computational thinking. You’ll begin with decomposition, breaking down a complex problem into smaller, simpler problems. Then through pattern recognition, you’ll compare the problem to other similar problems that have been solved previously.

Afterwards, during data representation and abstraction, you’ll simplify the problem even more by identifying what characteristics of the problem are important and filtering out those that are not.

The last pillar of computational thinking, algorithms, forms the second section of the course. The course defines algorithms as a set of step-by-step instructions to solve a problem. With algorithms, you can teach the computer how to solve problems without explicitly telling them precisely how. Instead, your algorithm will be able to handle a number of different cases, as long as these satisfy some preconditions.

You’ll explore a variety of algorithms, like linear and binary search. You’ll learn how to represent algorithms with flowcharts, analyze the complexity of algorithms (Big O), and calculate the number of possible solutions to an optimization problem. Lastly, you’ll compare the benefits and limitations of common algorithmic approaches to problem solving.

The third part of the course gives a brief history of computers, before settling on the computer architecture used by modern computers — the Von Neumann Architecture. 

It consists of three fundamental units: the memory, CPU, and I/O. You’ll learn how data and instructions are stored and accessed in computers as bits and bytes, and also how executing code amounts to moving pieces of data in memory and operating on it in the CPU.

In the fourth and final section, the course will instruct you on the basics of Python programming. You’ll explore iterations, classes, and debugging. And you’ll end the course by coding your own Python program, where you’ll get to implement the algorithms you learned previously into code.

The course is 4 weeks long, with each week having about 18 hours of course material. You’ll learn primarily from video lectures, and after each video there’ll be a short quiz to test your recall. There is supplementary material available on math, for those not-so-confident in their mathematical abilities.

At the end of each week, you’ll be presented with a case study where you’ll see examples of computational thinking used to solve real-life problems. Afterwards, you’ll complete a project where you’ll apply what you’ve learned. Do note that the assessments in this course are for verified learners.

• This course is endorsed by Google , which decided to make it part of its Digital Garage, a collection of courses and resources for learners wanting to gain tech skills.
• Penn’s Prof Susan Davidson, the course instructor, was named a Fellow of the American Association for the Advancement of Science in 2021.
• Prof. Davidson also teaches some of the courses of Penn’s Master of Computer and Information Technology (MCIT), which is offered online through Coursera.

3. Introduction to Computer Science and Programming Using Python (Massachusetts Institute of Technology)

My third pick for the best computer science course is Introduction to Computer Science and Programming Using Python , offered by MIT on edX.

This course approaches the field of computer science and programming through Python. The course focuses on breadth rather than depth, giving students background knowledge on the numerous applications of computation.

So this course is similar to our first pick in that it’s a survey course: it covers a lot, but not in great detail. But it’s dissimilar in that it focuses entirely on one programming language, Python, while Harvard’s course involves multiple languages.

Depending on your goals, this focus on Python could be seen as a positive or a negative. For what it’s worth, I believe Python is an excellent first programming language.

Heads up! This course tries to mirror the MIT on-campus experience, so don’t expect it to be a cakewalk. You won’t need any prior experience with computer science or programming to take it, but you’ll need a background in high school mathematics.

The main topics the course explores are computational thinking, data structures, iteration and recursion, decomposition, abstraction, and algorithms and complexity.

You’ll be given a brief introduction to computation and computational thinking. You’ll learn what computers are, how they work, and what their limitations are.

By understanding that computers only know what you tell them (and what they can infer from what you tell them), you’ll realize that in order for the computer to accomplish a task, they need a ‘recipe’ containing a sequence of instructions they should follow. This is what computer scientists call an algorithm.

Your programming journey begins by learning Python and its basic syntax. With Python, you’ll explore concepts common to most programming languages. These include variables, conditional statements, and control flows.

Furthermore, you’ll be introduced to functions and the role they play in decomposition, abstraction, and recursion, which are concepts fundamental to problem-solving in computer science.

By then, you should be able to code simple programs that can come up with approximate solutions to difficult math equations through a guess-and-check method.

Lastly, you’ll learn about the different ways we can represent information in Python, called data structures. You’ll work with lists, tuples, and dictionaries, and understand when to use one data structure over another.

The course is 9 weeks long with an expected workload of 14 to 16 hours each week. The main mode of learning is video lectures, and the course includes plenty of activities to put your hard-earned skills into practice. You’ll also have access to a learner’s forum where you can discuss with fellow learners.

There are 3 problem sets containing challenging coding exercises that will help you solidify your knowledge. If you are a verified learner, you’ll have to complete a timed mid-term and final exam in order to receive your certificate.

• This course has over 18k bookmarks and 120 reviews on Class Central.
• It is the first of a two-course XSeries Program on edX. The second is Introduction to Computational Thinking and Data Science , which could make for a good follow-up.
• One of the instructors, Professor John Guttag, leads the Data Driven Inference Group at MIT’s legendary Computer Science and Artificial Intelligence Laboratory (CSAIL).

4. Principles of Computing (Part 1) (Rice University)

Principles of Computing (Part 1), by Rice University on Coursera, is my fourth pick for the best computer science introduction. The course emphasizes doing rather than watching, requiring you to complete many coding assignments.

This course aims to help you step up your programming skills by teaching you computational problem solving, a skill that underlies computer science, and that was also the focus of our second pick . This will involve learning important programming practices and developing a mathematical foundation for problem solving.

To take this course, you’ll need to be comfortable with writing small (100+ lines) programs in Python, as well as have some background in high school mathematics. So this one doesn’t start from scratch, and is therefore geared toward learners that also have some basics down.

If you’re looking for a problem solving course with fewer prerequisites, you might want to have a look at our second pick .

The course includes refreshers on Python, code testing, probability and randomness, combinatorics, and function growth.

After a brief review of Python, the course will explain how to build tests, and why having tests for your Python programs can be useful.

Many programmers dislike or don’t simply bother to write tests for their code, but as one of the instructors explains, it’s a best practice worth treating as an integral part of the programming process.

Writing tests will help you save time and effort, and serves as a reusable sanity check that your program actually does what it’s supposed to do. For your first mini-project, you’ll recreate the well-known game 2048 in Python.

Then, the course moves on to the role of probability and randomness in computer science. You’ll learn how to identify unreasonable outcomes in probability, along with calculating the expected value of multiple outcomes.

For example, what’s the chance that a die would roll seven sixes out of ten tosses? And if that were to happen, to what extent could we conclude that the die is weighted — that is, that the rolls were unfair?

You’ll also see how we can use Python to simulate the probability of outcomes, a valuable tool used in statistical modeling. And for your second mini-project, you’ll work with probabilities to create an opponent that you can face in a game of Tic-Tac-Toe.

The course also touches on combinatorics, which deals with enumerations, permutations, and combinations. You’ll figure out how to calculate the total number of ways an event can play out.

This helps greatly in calculating the number of steps an algorithm would take, thereby allowing you to estimate the running time of the algorithm, and in turn, determine if the algorithm would be worth implementing. You can see why combinatorics plays a major role in password and computer security. For your third mini-project, you’ll code the familiar dice game Yahtzee .

In the final part of the course, you’ll be taught the importance of counting in solving complex problems. Counting answers the question of how long an algorithm might take to run given a task. Another name for counting you might be more familiar with is “time complexity”.

You’ll also learn about higher-order functions in Python, that is functions that take other functions as algorithms, like the map function. In your last mini-project, you’ll use these concepts to make your own version of Cookie Clicker .

The course is split into 5 weeks, with each week involving 7 to 10 hours of study. You’ll learn primarily through video lectures and graded assignments, although the course does supply supplemental notes and activities for further reading and practice.

You’ll code and submit the homework and mini-projects on their companion website CodeSkulptor , and in-browser code editor that will preempt the need of setting up a local coding environment.

• The course has around 15k bookmarks on Class Central.
• This course is the third of seven courses that make up the Fundamentals of Computing Specialization . Upon receiving the specialization certificate, you’ll have completed 20+ projects, including a capstone project.
• If you’re not interested in taking a full specialization after this course, but you’d like to learn more about the course topic, as the course name implies, there’s a follow-up course: Principles of Computing (Part 2) .
• Course instructor Prof. Scott Rixner is faculty director of two online degree programs at Rice University. So his dedication to online education extends beyond the scope of his own MOOCs.

5. Computer Science 101 (Stanford University)

Computer Science 101 aims to demystify the magic of computers by demonstrating that they work by following a few relatively simple patterns.

This course will help you become familiar with those patterns. It will give insights into how computers work and what their limitations are.

In addition, the course delves into networking and other major topics within CS. No prior knowledge of computer science required!

The course starts off with the fundamental equation of computers: Computer = Powerful + Stupid. Computers are powerful because they can perform billions of operations per second. But they are stupid because they need someone to tell them what to do. This is where programmers come into play.

This course uses small snippets of JavaScript to introduce you to programming and other computer science concepts. You’ll gain a grasp of programming concepts like variables, loops and iterations, conditional statements, and so on. The course later covers low-level and high-level languages, as well as compilers and interpreters.

The computer is a tool and the programmer wields the tool. Therefore, to program efficiently, it is important to understand how the tool works. The course covers many aspects of said tool, including hardware. You’ll learn about the parts that make up a computer, and look at how computers can represent different information formats.

The main format you’ll work with is images. One of the things you’ll do is “greenscreen” images as well as turn coloured images into grayscale by operating at the individual pixel level.

Another topic the course covers is computer networks, which is how computers communicate with one another. You’ll learn about the different types of networks.

You’ll study what IP addresses are and how they allow computers to locate each other. The course discusses how computers transmit information through data packets, and also the communication protocol the Internet runs on  —  TCP/IP.

The course also briefly covers a variety of other topics like databases and spreadsheets, computer security, and analog and digital data.

The course is 6 weeks long, with each week taking 4–6 hours to complete. Lessons are provided through video lectures and are supplemented with notes and assessments. However, you’ll need to be a verified learner to access the assessments.

• The instructor acknowledges Google for supporting his early research into creating the class. I think this goes for all of us!
• This course has 3k bookmarks on Class Central.
• The course instructor Nick Parlante’s current interest is in CodingBat Java , an experimental online code-practice tool.

6. How Computers Work (University of London)

This concise course taught by the University of London on Coursera touches on a few key topics in computer science, but it is mostly interested in helping you build a foundational understanding of hardware. It’s in the title really: by the end of the course, you’ll know how computers work.

And through that understanding, you’ll also form a clearer picture of how computers can be leveraged to help solve everyday problems.

The course is just as suitable for someone wanting to build solid foundations for further study in CS, as it is for someone simply curious about how computers work and wanting to explore some key CS topics but not necessarily a deep dive.

You do not need any prior knowledge of computer science to take this course.

This course covers computer hardware, abstraction, modularity, computer networks and communication.

The course begins with abstraction — the art of drawing attention to the important details while filtering out the noise. Many disciplines rely on abstraction, and computer science does so heavily, both at the hardware and software levels.

This concept will become apparent when the course starts discussing computer hardware, like memory, CPU, and other devices. You’ll use notional machines as means for capturing these abstractions.

Afterwards, you’ll move on to another key idea: state and modularity. This will help you answer the question, ‘Why does turning off and on my computer fix most problems?’

Using notional machines, you’ll explain how computer applications function by transitioning through different states, and how modularity allows them to interact with other applications. You’ll learn how to debug stuff, a very useful skill indeed.

Moving on, you’ll learn how computers talk to one another over the Internet through networks and communication protocols. You’ll also learn about the kinds of security threats computers (and users) face, and how to protect yourself from malicious actors.

Lastly, you’ll explore basic web development. By applying your new-found knowledge of abstraction, state, and modularity, you’ll be able to clearly understand how websites work.

The course is 4 weeks long, with 10 hours worth of material per week. It consists of video lectures and quizzes to test your knowledge of the material. You’ll have the chance to share your thoughts in discussion prompts.

• The course instructor, Prof. Marco Gillies , is the Academic Director of Distance Learning at Goldsmiths, University of London.
• This course is an introduction to the University of London’s Online Bachelor of Computer Science , offered on Coursera.
• It is course two out of three of the Introduction to Computer Science and Programming Specialization , with the first course being Introduction to Computer Programming .

7. CS50's Understanding Technology (Harvard University)

This is another course from the CS50 family. But unlike our first pick, which is the main CS50 course, this course is for those who work with technology everyday but don’t understand how it all works under the hood or how to solve problems when something goes wrong. And it’s also for those who don’t (yet) work with technology — most notably, computers — but would nonetheless like to understand its functioning.

The course aims to fill in the gaps in your knowledge of hardware, internet, multimedia, programming, and web development, preparing you for the technology of today and tomorrow.

This course has no prerequisites.

The course begins with an introduction to the language of computers, binary. It explains how computers use binary to represent text and other information. Then, you’ll move on to the hardware of the computer: CPU, RAM and Main Memory. You’ll learn about the functions of each of these components.

The course discusses Internet and multimedia, and the technologies underpinning them. It’ll tell you how computers can find and talk with one another. You’ll learn about the common Internet protocol TCP/IP and more.

You’ll learn about the different data representations of multimedia, like audio, images, and video. There are many file formats and compression techniques – the course will give you an overview of some of the main ones.

Next, you’ll be taught how to stay safe on the Internet. You’ll discover several ways to protect your data and privacy. This section will include lessons on cookies, passwords, two-factor authentication, encryption, and more.

You’ll continue with the basics of web development. You’ll learn how web browsers access the web with HTTP requests. Have you ever seen a 404 or 500 error when trying to visit a webpage? You probably have. Well, in this course, you’ll learn what these errors mean. A brief overview on the languages that allow us to build and style web pages, HTML and CSS, is provided.

Last by not least, you’ll discover the basics of programming. You’ll primarily use the block-based language Scratch to explore concepts common to pretty much all programming languages, like variables, expressions, loops, and so on.

Additionally, to demonstrate what an algorithm is (and more specifically the divide-and-conquer paradigm ), you’ll watch the instructor tear a phonebook into halves… I had to mention this because it is both very instructive and memorable!

The course is 6 weeks long, with each week taking 2 to 6 hours to complete, depending on your prior familiarity with the content. Each week contains at least one hour of lecture.

Regarding assessments, you’ll have to complete an assignment for each of the six topics presented in the course to earn a certificate.

• After taking this course, you’ll be more than ready to tackle CS50, our #1 pick .
• This course has 1.6k bookmarks on Class Central.
• Another fact about David J. Malan, the course instructor: he is an active member of the SIGCSE , the arm of the ACM concerned with computer science education.

8. Intro to Theoretical Computer Science (Udacity)

For those who have some familiarity with programming and algorithms, and want to further their understanding of problem-solving in computer science, this rigorous but insightful course might be what you’re looking for.

Offered by Udacity, Intro to Theoretical Computer Science explores what makes a problem ‘hard’ to solve, even for a computer. Then, it shows how to reduce and simplify these ‘hard’ problems to make them easier to solve through computation.

The course covers two main areas of theoretical computer science: complexity theory and computability.

Complexity theory asks how much of its resources, like time or memory, will a computer require to solve a problem. Computability, on the other hand, asks if a computer can solve a problem at all, even when given more time and memory.

The course introduces you to a variety of real-world problems from telecommunication, bioinformatics, and finance. You will recognize what makes a problem challenging, and the value of recognizing such problems. This will prime you for understanding what NP-completeness is. Then, you’ll understand what makes a problem ‘hard’ to solve, and be able to prove it.

The rest of the course discusses what to do with the problem once we’ve proved that it is hard (or even impossible to solve).

One of the ways to overcome this obstacle is to employ efficient, intelligent algorithms. Another way is to accept that the problem may not be perfectly solvable, and instead find an approximate solution. And yet another way is to use randomness and probability to poke around and find a solution.

You’ll be able to describe and use these techniques in practical situations: the course discusses the theory but it’s also hands-on.

Lastly, you’ll move on to problems that no computer can ever solve in theory. You will learn about undecidability and recognize the limits of computability.

The course is 8 weeks long, with a total 14 hours of video lectures. Some videos have a quiz to help you practice recalling what you’ve learned. There are 7 chapters, and at the end of each chapter you will complete a problem set to put your new-found skills to good use.

Finally, there’s a summative exam at the end of the course.

• This course has 2.2k bookmarks on Class Central.
• One of the course instructors, Sebastian Wernicke, has spoken multiple times at TED .
• To tackle this course, you may want to learn about algorithms first. The instructors recommend another Udacity course on algorithms as a refresher. In addition, good math foundations would be useful too. Check our picks below if needed.

9. Mathematics for Computer Science (University of London)

Offered by the University of London, this course introduces you to the mathematics and mathematical thinking computer scientists use in their work. What distinguishes this course from other math courses is its playfulness, with fun and interactive exercises.

More specifically, the course combines elements of algebra, analysis, and geometry — topics carefully picked to serve as the backbone of your computer science education.

The course discusses, among others, number bases, an essential topic to understand binary, and conversion between binary and other bases, such as hexadecimal. It explores numerical progressions, like the well-known Fibonnaci sequence. And it will touch on geometry and function graphing.

By the end of the course, you’ll have acquired the foundation needed to understand the math that underpins other computer science courses, and you’ll be ready to tackle more advanced mathematical topics.

The course assumes you know some high school mathematics as well as basic Python programming.

The course investigates five main topics: number bases, modular arithmetic, sequences, series, graph sketching and kinematics.

The course begins with the study of number bases. You might know that binary is the number base used by computers. But did you know that computer scientists also use hexadecimals?

You’ll cover the key concepts of place values and number systems, which will involve converting between binary, hexadecimal, and decimal, as well as adding, subtracting, and multiplying them together. Oh, a cool thing that the course teaches you is steganography, the art of hiding messages in images!

Next, you’ll cover modular arithmetic. Have you ever wondered what “modulo 7” means? You’ll learn about the usefulness of congruence and modular arithmetic operations in computer science (psst, it can be used for encryption).

You’ll identify, describe, and compute sequences of numbers and their sums. You’ll study a special family of sequences called progressions, which consists of arithmetic and geometric progressions. You’ll learn how sequences can be used to generate random numbers. Additionally, you’ll be able to tell when a series converges (meets at a point) or diverges (approaches infinity)

Lastly, the course describes how to represent and describe space numerically using coordinates and graphs. You’ll see how graphs can help us visualize and transform functions like straight lines, quadratics, cubics, reciprocals and more. An example of modeling motion will be given: the field of mathematics called kinematics.

The course is 6 weeks long, with about 40 hours worth of material. Each week comes with one or more quizzes, allowing you to learn by doing. However, you’ll need to pay for the certificate for the course autograder to mark your answers.

• It is the third and final course of the Introduction to Computer Science and Programming Specialization .
• Dr. Sara Santos enjoys math busking , which seeks to surprise and amuse people on the streets with performances rooted in math.

10. Mathematics for Computer Science: Essential Skills (University of Hull)

If you have taken a look at the previous two courses but do not have the mathematical foundations to take them yet, this course can help you with the basics.

This course is a short course on mathematics skills for computer science, offered by the University of Hull on FutureLearn.

Meant for learners starting or considering studying computer science at the university level, this course covers Venn diagrams and set theory, algebra techniques, and vectors and matrices — all fundamentals concepts ubiquitous in computer science.

The course assumes no prior mathematical knowledge. You’re starting from scratch.

Starting off with Venn diagrams and set theory, you’ll learn how “sets” (bags of objects, if you will) can be formalized and operated on. You’ll learn to reason about computations and objects of computation. Venn diagrams will help you visualize this type of reasoning.

You’ll then move on to algebra and its techniques. You’ll be given an overview of algebra (which could be described as doing math using variables instead of explicit numbers) and its use in algorithms and scientific computation. The course will teach you how to solve linear equations and quadratic equations using algebra.

The course ends with an overview of vectors and matrices. You’ll learn what vectors are, and why they are especially important in graphics programming. You’ll learn how we can represent vectors as matrices, and how to modify, transform, and invert matrices to solve complex problems.

This course is 3 weeks long, with around 3 hours of material per week. You’ll learn primarily through video material, although there are discussion forums where you can discuss problems with fellow learners.

At the end of each week, there is a quiz that’ll help you strengthen your understanding of mathematical concepts and applications.

• The course instructor, Laura Broddle , joined the University of Hull in 2015 as a foundation math teaching fellow.
• She also had visited a sister school in Uganda and was rated an outstanding teacher by Ofsted in 2013.

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Harvard CS50 – Full Computer Science University Course

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⌨️ Lecture 0 - Scratch ⌨️ Lecture 1 - C ⌨️ Lecture 2 - Arrays ⌨️ Lecture 3 - Algorithms ⌨️ Lecture 4 - Memory ⌨️ Lecture 5 - Data Structures ⌨️ Lecture 6 - Python ⌨️ Lecture 7 - SQL ⌨️ Lecture 8 - HTML, CSS, JavaScript ⌨️ Lecture 9 - Flask ⌨️ Lecture 10 - Emoji ⌨️ Cybersecurity

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• Mohammad Ilham @ilhamsiddiq 3 months ago Dalam kursus Harvard CS50, Saya mempelajari berbagai topik yang mencakup dasar-dasar pemrograman dan konsep-konsep teknis. Berikut adalah rangkuman dari beberapa topik yang Saya dapatkan: Scratch: Kursus dimulai dengan Scratch, platform pemrograman… Read more Dalam kursus Harvard CS50, Saya mempelajari berbagai topik yang mencakup dasar-dasar pemrograman dan konsep-konsep teknis. Berikut adalah rangkuman dari beberapa topik yang Saya dapatkan: Scratch: Kursus dimulai dengan Scratch, platform pemrograman visual yang dirancang untuk memperkenalkan konsep-konsep dasar pemrograman kepada pemula, seperti algoritma, kontrol alur, dan logika pemrograman. Bahasa C: Saya mempelajari bahasa pemrograman C, termasuk sintaks dasar, tipe data, struktur kontrol, fungsi, dan pengelolaan memori. C merupakan bahasa dasar yang penting dalam pemrograman komputer. Array: Saya belajar tentang array, struktur data yang menyimpan kumpulan elemen data yang sama jenisnya. Saya belajar bagaimana mengakses, mengubah, dan mengelola array dalam pemrograman. Algoritma: Saya mempelajari konsep dasar algoritma, termasuk pencarian, pengurutan, dan analisis kompleksitas algoritma. Saya belajar bagaimana merancang dan menganalisis algoritma untuk menyelesaikan berbagai masalah. Memory: Saya mempelajari tentang manajemen memori dalam pemrograman, termasuk alokasi dan dealokasi memori, pointer, dan segmen memori. Ini penting untuk memahami bagaimana program bekerja secara internal dan menghindari bug memori. Struktur Data: Saya mempelajari berbagai struktur data, seperti linked list, stack, queue, dan tree. Saya belajar bagaimana menggunakan struktur data ini untuk menyimpan dan mengorganisir data dengan efisien. Python: Saya diperkenalkan pada bahasa pemrograman Python, yang populer karena sintaksnya yang mudah dipahami dan beragamnya penggunaan. Saya mempelajari dasar-dasar Python serta bagaimana menggunakannya untuk menyelesaikan berbagai masalah. SQL: Saya mempelajari bahasa kueri SQL untuk mengakses dan memanipulasi basis data. Saya belajar tentang pembuatan dan pengelolaan tabel, penggunaan kueri SELECT, INSERT, UPDATE, DELETE, serta konsep-konsep lain dalam database. HTML, CSS, JavaScript: Saya belajar tentang dasar-dasar pengembangan web, termasuk HTML untuk struktur halaman web, CSS untuk tata letak dan gaya, dan JavaScript untuk interaktivitas dan dinamika. Flask: Saya mempelajari Flask, sebuah framework web untuk Python, yang memungkinkan Saya membuat aplikasi web dengan mudah. Saya belajar tentang routing, template, form, dan integrasi basis data dalam Flask. Emoji: Saya mempelajari bagaimana menggunakan emoji dalam pemrograman, termasuk bagaimana mengintegrasikannya ke dalam kode dan menggunakannya untuk meningkatkan pengalaman pengguna. Keamanan Siber (Cyber Security): Saya mempelajari konsep dasar keamanan siber, termasuk enkripsi, autentikasi, otorisasi, serangan siber umum, dan praktik-praktik keamanan yang baik dalam pengembangan perangkat lunak. Ini adalah rangkuman singkat dari beberapa topik yang Saya pelajari dalam kursus Harvard CS50. Setiap topik memberikan Saya landasan penting dalam pemrograman dan teknologi informasi yang dapat diterapkan dalam berbagai konteks dan industri. Helpful
• Platon Loveyko @platonloveyko 1 year ago As an aspiring computer science enthusiast, I recently embarked on an incredible learning journey with Harvard CS50 through freeCodeCamp, and I must say it has been an exceptional experience. This full computer science university course has left a l… Read more As an aspiring computer science enthusiast, I recently embarked on an incredible learning journey with Harvard CS50 through freeCodeCamp, and I must say it has been an exceptional experience. This full computer science university course has left a lasting impact on my understanding of the subject, and I cannot praise it enough. First and foremost, the course content was masterfully crafted. It covered a wide range of topics, starting from the fundamentals of programming all the way to complex data structures and algorithms. The instructors at Harvard University have a remarkable talent for explaining intricate concepts in a clear and engaging manner. They made sure to strike a perfect balance between theory and hands-on exercises, which kept me actively involved throughout the course. One aspect that sets Harvard CS50 apart is the emphasis on problem-solving and critical thinking. The problem sets provided challenged my abilities and encouraged me to think creatively to come up with innovative solutions. The course's practical approach allowed me to apply the knowledge gained to real-world scenarios, instilling a sense of confidence in my programming skills. Furthermore, the freeCodeCamp platform was user-friendly and intuitive, making navigation seamless and hassle-free. The interface allowed me to access lectures, problem sets, and additional resources conveniently, which was crucial for maintaining a steady pace in my learning journey. Another highlight was the supportive community that surrounded the course. I was pleasantly surprised by the active online forums and discussion groups, where fellow learners from around the world shared insights, tips, and encouragement. This sense of camaraderie motivated me to push my limits and achieve more than I thought possible. In conclusion, Harvard CS50 via freeCodeCamp is undoubtedly a gem in the realm of computer science courses. The depth and quality of instruction, combined with a supportive learning environment, made it an unforgettable experience. I now feel well-equipped with a solid foundation in computer science, thanks to this extraordinary course. Whether you are a beginner or a seasoned programmer looking to expand your knowledge, I wholeheartedly recommend Harvard CS50 as an essential part of your learning journey. Kudos to Harvard University and freeCodeCamp for providing this incredible opportunity! Helpful
• Sufian Mohidin @sutrasarawak 6 months ago As a Sarawakian artist, completing this course has unfolded an odyssey filled with revelations. Harvard CS50, in its own right is like a transformative venture, adeptly intertwining the intricate threads of creativity and technology into an expan… Read more As a Sarawakian artist, completing this course has unfolded an odyssey filled with revelations. Harvard CS50, in its own right is like a transformative venture, adeptly intertwining the intricate threads of creativity and technology into an expansive tapestry of understanding that surpassed my initial expectations. In my mother tongue, I would say: Pecah palak, tapi tajam otak sik dapat dibulak bah! - Lets leave that as a mystery. Only Sarawakians would know. Moving on... I know and I can tell you why Harvard is the bomb. This power lies in its instuctors. Let me tell you this- These master deserve all the commendations for their adeptness in simplifying intricate concepts. David J. Malan, the maestro behind the pedagogical magic, possesses an uncanny ability to demystify the enigmatic languages of coding and algorithms. His teaching style, pulsating with energy and engagement, transcended the confines of the screen, transforming the intricate dance of 0s and 1s into strokes on the canvas of my artistic expression. As a Sarawakian artist navigating the unfamiliar terrain of coding, this approach proved to be invaluable. The course's structure, inaugurating with Scratch – a visual programming language – felt like a stroke of brilliance. It mirrored the process of mastering the rudiments of sketching before venturing into the creation of a masterpiece. This gradual ascent facilitated my transition into more intricate languages like C and Python, akin to the layered application of colors on my artistic canvas, imbuing me with newfound confidence. However, the course did present its share of challenges. The relentless pace necessitated unwavering dedication, with certain concepts demanding multiple revisits for comprehensive understanding. Yet, surmounting these obstacles yielded a profound sense of accomplishment, reminiscent of refining a particularly intricate brushstroke technique. In conclusion, Harvard CS50 transcends the conventional definition of a computer science course; it unfolds as a voyage that spans geographical boundaries, forging connections between artists and technologists. For a Sarawakian artist like myself, it manifested as a journey that expanded my horizons, seamlessly amalgamating the hues of traditional art with the pixels of the digital canvas. It serves as a testament to the universality of knowledge and the transformative potential inherent in education. Thank you Helpful
• Sk Md Yasin 11 months ago Harvard's CS50, also known as "Introduction to Computer Science," is one of the most renowned and well-regarded computer science courses in the world. As of my last knowledge update in September 2021, I can provide you with a comprehensive review of… Read more Harvard's CS50, also known as "Introduction to Computer Science," is one of the most renowned and well-regarded computer science courses in the world. As of my last knowledge update in September 2021, I can provide you with a comprehensive review of the course based on its reputation and content up to that point. **Course Overview:** CS50 is an introductory computer science course designed to provide students with a solid foundation in computer science and programming. It's taught by Professor David J. Malan and is part of Harvard University's curriculum. The course is notable for its rigorous approach to teaching computer science and its engaging lectures. **Key Highlights:** 1. **Engaging Lectures:** Professor Malan is known for his dynamic and engaging teaching style. His lectures are not only informative but also entertaining, making complex topics more accessible. 2. **Diverse Curriculum:** CS50 covers a wide range of topics, including computer science fundamentals, algorithms, data structures, web development, and more. This breadth ensures that students get exposure to various aspects of computer science. 3. **Problem-Solving Emphasis:** The course places a strong emphasis on problem-solving skills, which are essential in the field of computer science. Students are challenged with problem sets and projects that require creative thinking and programming skills. 4. **Supportive Community:** CS50 has a strong online community of students and enthusiasts who can help with problem-solving and offer support. The course has a dedicated subreddit and discussion forums. 5. **Online Accessibility:** The course is made available online for free through edX, allowing individuals worldwide to access the lectures, problem sets, and other course materials. This has contributed to its popularity and accessibility. 6. **Diverse Resources:** In addition to lectures, CS50 provides supplementary resources such as walkthroughs, shorts (short video clips explaining concepts), and a variety of programming languages, including C, Python, SQL, and more. 7. **Challenging Problem Sets:** CS50 is known for its challenging problem sets that push students to apply what they've learned in lectures. Completing these assignments is crucial for a deep understanding of the material. 8. **Final Project:** The course culminates in a final project where students have the opportunity to design and build a significant software application. This project allows students to showcase their skills and creativity. **Potential Drawbacks:** 1. **Intensive Workload:** CS50 can be quite demanding, especially for beginners with no prior programming experience. The problem sets and projects can be time-consuming. 2. **Pacing:** Some students might find the course's fast pace challenging, especially if they are new to programming and computer science concepts. 3. **C Language:** The course starts with the C programming language, which can be challenging for beginners due to its low-level nature. However, this also provides a strong foundation in computer science principles. 4. **Self-Motivation:** As an online course, CS50 requires students to be self-motivated and disciplined. Keeping up with the lectures and assignments can be challenging for some. In summary, CS50 is an excellent computer science course that provides a strong foundation in the field. It's known for its engaging lectures, comprehensive curriculum, and challenging problem sets. However, it can be demanding, especially for beginners, and requires dedication and self-discipline. If you are interested in learning computer science, it's definitely worth considering, whether you're a Harvard student or an online learner. Be sure to check for any updates or changes to the course content or structure since my last knowledge update in September 2021. Helpful
• Paulo 6 months ago Critical Review of Harvard CS50 - Introduction to Computer Science Introduction: Harvard CS50 - Introduction to Computer Science is an excellent opportunity for those who want to learn the fundamentals of computer science, regardless of their pri… Read more Critical Review of Harvard CS50 - Introduction to Computer Science Introduction: Harvard CS50 - Introduction to Computer Science is an excellent opportunity for those who want to learn the fundamentals of computer science, regardless of their prior experience. The course is taught by David Malan, a professor of computer science at Harvard, and offers a comprehensive introduction to various topics, including: Programming: C, Python, Java, JavaScript, HTML, CSS, and SQL Algorithms: Search, sorting, data structures Computer networks: TCP/IP, HTTP, DNS Computer security: Cryptography, authentication, attacks Web development: Front-end, back-end, databases Artificial intelligence: Machine learning, natural language processing Computing ethics: Algorithmic bias, privacy, intellectual property Strengths of the course: Comprehensive and well-structured content: The course covers a wide range of topics, from programming fundamentals to more advanced areas such as artificial intelligence and computing ethics. The content is presented in an organized and progressive manner, facilitating learning. High-quality teaching materials: The course offers high-quality teaching materials, including video lectures, lectures, exercises, projects, and quizzes. The video lectures are particularly well-made, with clear and concise explanations, as well as practical examples that help illustrate the concepts. Experienced and accessible instructors: The course is taught by experienced and accessible instructors who are always available to answer questions and help students. In addition to video lectures, the course offers online forums and live tutoring sessions where students can interact with instructors and other students. Vibrant community: The CS50 course has a vibrant community of students from all over the world. This community is a great resource for students, as it provides a space to exchange ideas, ask questions, and collaborate on projects. Certificate of completion: Upon completion of the course, students receive a certificate of completion from Harvard University. This certificate can be an important differentiator on a professional's resume. Weaknesses of the course: High workload: The CS50 course is an extensive course, approximately 12 weeks long with an estimated workload of 10 to 12 hours per week. This can be challenging for those with other responsibilities, such as work or school. Difficulty for beginners: The CS50 course is an introductory course, but it can be challenging for those with no programming experience. The first modules of the course require some effort to keep up with the pace of the classes and complete the exercises. Lack of focus on specific areas: The CS50 course offers a comprehensive introduction to computer science, but does not go into depth in specific areas. If you want to specialize in a specific area, such as web development or artificial intelligence, you will need to seek out other courses or resources. Conclusion: Harvard CS50 - Introduction to Computer Science is an excellent option for those who want to learn the fundamentals of computer science and gain a solid foundation for further studies or a career in the field. The course offers comprehensive and high-quality content, experienced instructors, and a vibrant community. However, it is important to keep in mind that the course requires dedication and can be challenging for beginners. Recommendations: Assess your time availability and knowledge level before enrolling in the course. Actively participate in classes, online forums, and tutoring sessions. Supplement the course with other resources, such as books, tutorials, and online courses, to go deeper into specific areas. Helpful
• Ronne Amaro @RonneAmaro 3 weeks ago Meu nome é Ronne Maicon Amaro dos Reis. Sou formado em Análise e Desenvolvimento de Sistemas (2017), pós-graduado em MBA Gerenciamento de Projetos T.I (2021) e em Docência Profissional e Tecnológica, com título de Especialista (2022). Atualmente, tr… Read more Meu nome é Ronne Maicon Amaro dos Reis. Sou formado em Análise e Desenvolvimento de Sistemas (2017), pós-graduado em MBA Gerenciamento de Projetos T.I (2021) e em Docência Profissional e Tecnológica, com título de Especialista (2022). Atualmente, trabalho como professor de tecnologia. Recentemente, concluí o curso "Harvard CS50 – Curso Universitário Completo em Ciência da Computação" e gostaria de compartilhar minha experiência. O curso CS50 é uma introdução abrangente à ciência da computação e à arte da programação. Ministrado pelo professor David J. Malan, o curso cobre uma variedade de tópicos essenciais, incluindo algoritmos, estruturas de dados, segurança, desenvolvimento web e muito mais. Pontos Positivos: Qualidade do Ensino: O professor Malan é excepcional na maneira como apresenta o conteúdo. Suas explicações são claras e envolventes, facilitando o entendimento de conceitos complexos. Abrangência do Conteúdo: O curso aborda uma ampla gama de tópicos, proporcionando uma visão completa e aprofundada da ciência da computação. Hands-on: A ênfase em projetos práticos e desafios de programação ajuda a solidificar o aprendizado e a desenvolver habilidades práticas essenciais. Recursos Disponíveis: O curso oferece uma abundância de recursos, incluindo vídeos, anotações, fóruns de discussão e suporte da comunidade. Pontos a Melhorar: Idioma: Seria de grande valia se os curso tivesse legenda em outros idiomas para melhor disseminação para um numero maior de possoas. Nível de Dificuldade: Alguns tópicos podem ser bastante avançados para iniciantes completos. Um conhecimento prévio básico em programação pode ser útil. Em resumo, o CS50 é um curso excepcional que oferece uma sólida base em ciência da computação. Recomendo fortemente para qualquer pessoa interessada em aprofundar seus conhecimentos na área, seja um iniciante ou alguém buscando aprimorar suas habilidades. Helpful
• RANA SMIT HEMTANTBHAI @thesmitrana 2 weeks ago CS50, offered by Harvard University through edX and taught by Professor David J. Malan, is a highly acclaimed introductory computer science course that provides a comprehensive foundation in programming and problem-solving. Covering languages like C, Python, SQL, and JavaScript, as well as web development basics, the course is well-structured with engaging lectures, challenging problem sets, and hands-on projects, including a final project. Despite being time-consuming and demanding, the course is widely accessible, with robust support and an active online community, making it an excellent choice for both beginners and those looking to enhance their coding skills. i learned so much and it will be so helpfull Helpful
• آدم حسين محمد موسى 6 months ago بصفتي متحمسا طموحا لعلوم الكمبيوتر ، شرعت مؤخرا في رحلة تعليمية لا تصدق مع Harvard CS50 من خلال freeCodeCamp ، ويجب أن أقول إنها كانت تجربة استثنائية. لقد تركت هذه الدورة الجامعية الكاملة لعلوم الكمبيوتر تأثيرا دائما على فهمي للموضوع ، ولا يمكنني ال… Read more بصفتي متحمسا طموحا لعلوم الكمبيوتر ، شرعت مؤخرا في رحلة تعليمية لا تصدق مع Harvard CS50 من خلال freeCodeCamp ، ويجب أن أقول إنها كانت تجربة استثنائية. لقد تركت هذه الدورة الجامعية الكاملة لعلوم الكمبيوتر تأثيرا دائما على فهمي للموضوع ، ولا يمكنني الثناء عليها بما فيه الكفاية. أولا وقبل كل شيء ، تم تصميم محتوى الدورة ببراعة. غطت مجموعة واسعة من الموضوعات ، بدءا من أساسيات البرمجة وصولا إلى هياكل البيانات والخوارزميات المعقدة. يتمتع المعلمون في جامعة هارفارد بموهبة رائعة في شرح المفاهيم المعقدة بطريقة واضحة وجذابة. لقد حرصوا على تحقيق توازن مثالي بين التمارين النظرية والعملية ، مما جعلني أشارك بنشاط طوال الدورة. أحد الجوانب التي تميز Harvard CS50 هو التركيز على حل المشكلات والتفكير النقدي. شكلت مجموعات المشكلات المقدمة تحديا لقدراتي وشجعتني على التفكير بشكل خلاق للتوصل إلى حلول مبتكرة. سمح لي النهج العملي للدورة بتطبيق المعرفة المكتسبة على سيناريوهات العالم الحقيقي ، وغرس الشعور بالثقة في مهاراتي في البرمجة. علاوة على ذلك ، كانت منصة freeCodeCamp سهلة الاستخدام وبديهية ، مما يجعل التنقل سلسا وخاليا من المتاعب. سمحت لي الواجهة بالوصول إلى المحاضرات ومجموعات المشكلات والموارد الإضافية بسهولة ، وهو أمر بالغ الأهمية للحفاظ على وتيرة ثابتة في رحلتي التعليمية. ومن المعالم البارزة الأخرى المجتمع الداعم الذي أحاط بالدورة. لقد فوجئت بسرور بالمنتديات النشطة عبر الإنترنت ومجموعات المناقشة ، حيث شارك زملائي المتعلمون من جميع أنحاء العالم الأفكار والنصائح والتشجيع. حفزني هذا الشعور بالصداقة الحميمة على دفع حدودي وتحقيق أكثر مما كنت أعتقد أنه ممكن. في الختام ، تعد Harvard CS50 عبر freeCodeCamp بلا شك جوهرة في مجال دورات علوم الكمبيوتر. إن عمق وجودة التعليم ، جنبا إلى جنب مع بيئة التعلم الداعمة ، جعلها تجربة لا تنسى. أشعر الآن أنني مجهز جيدا بأساس متين في علوم الكمبيوتر ، وذلك بفضل هذه الدورة غير العادية. سواء كنت مبتدئا أو مبرمجا متمرسا تتطلع إلى توسيع معرفتك ، فإنني أوصي بشدة بجامعة هارفارد CS50 كجزء أساسي من رحلة التعلم الخاصة بك. مجد لجامعة هارفارد و freeCodeCamp لتوفير هذه الفرصة الرائعة Helpful
• MUHAMMAD BIN ARSHAD 1 year ago Title: CS50: A Transformative Journey into Computer Science Rating: ★★★★★ CS50, offered by Harvard University, is an exceptional online course that delves deep into the realm of computer science. With its engaging lectures, comprehensive assignmen… Read more Title: CS50: A Transformative Journey into Computer Science Rating: ★★★★★ CS50, offered by Harvard University, is an exceptional online course that delves deep into the realm of computer science. With its engaging lectures, comprehensive assignments, and vibrant community, CS50 offers a transformative learning experience that sets it apart from other online courses in the field. As a passionate learner myself, I cannot recommend CS50 enough for anyone looking to explore the world of computer science or enhance their existing skills. One of the standout features of CS50 is its remarkable instructor, Professor David J. Malan. His enthusiasm for the subject matter is infectious, making even the most complex topics accessible and exciting. Professor Malan's ability to break down intricate concepts and explain them in a relatable manner truly sets this course apart. His engaging lectures, delivered with clarity and energy, ensure that learners remain captivated throughout their journey. CS50's curriculum is carefully designed to provide a comprehensive understanding of computer science fundamentals. From programming languages like C, Python, and SQL to topics such as algorithms, data structures, and web development, the course covers a wide range of essential concepts. The course strikes an ideal balance between theory and practical implementation, ensuring that learners not only grasp the concepts but also gain hands-on experience through assignments and projects. What sets CS50 apart is its commitment to fostering a strong learning community. The course employs a vibrant online platform where students can engage in discussions, seek help, and collaborate on projects. The supportive community, including both fellow learners and dedicated teaching assistants, creates an environment conducive to growth and collaboration. The forums are a treasure trove of knowledge, with participants actively helping each other overcome obstacles and providing valuable feedback. CS50's assignments and projects are thoughtfully crafted to challenge and inspire students. They provide a platform to apply the knowledge gained in lectures and reinforce key concepts. The course incorporates both individual and team-based projects, encouraging collaboration and fostering essential teamwork skills. The problem sets are rigorous yet rewarding, pushing learners to think critically and develop innovative solutions. Furthermore, the course's production quality is top-notch. The high-definition video lectures are accompanied by clear slides and subtitles, ensuring that learners can follow along effortlessly. The course's intuitive online platform provides easy access to all the necessary materials, including lecture videos, problem sets, and supplementary resources. While CS50 is an outstanding course, it's worth noting that it requires a significant time commitment and dedication. The assignments and projects can be challenging, especially for beginners. However, the course is structured in such a way that it gradually builds upon previous knowledge, ensuring that learners progress at a comfortable pace. In conclusion, CS50 is an exceptional online course that excels in every aspect. Whether you're a complete beginner or an experienced programmer, the course offers a comprehensive and engaging journey into the world of computer science. With its stellar instruction, well-designed curriculum, vibrant community, and practical projects, CS50 equips learners with the knowledge and skills necessary to thrive in the field. Embark on this transformative adventure, and you'll undoubtedly come out with a newfound passion for computer science. Helpful
• Mukhtar Saeed Adbdu @mukhtarsaeed 1 year ago thank you for coursesDo I need to run the model offline?” Data science has revolutionized the way data is perceived. There are many data science applications in healthcare, banking, e-commerce, manufacturing, and more. Big data companies like Amazon… Read more thank you for coursesDo I need to run the model offline?” Data science has revolutionized the way data is perceived. There are many data science applications in healthcare, banking, e-commerce, manufacturing, and more. Big data companies like Amazon, Google, and Facebook use data science concepts for business insights and decisions for their organizations. Data science projects are happening around the world, from predicting the side effects of medications to optimizing routes during traffic rush hours. Data science is used in a variety of industries and all kinds of organizations—some might even surprise you. Data science can: • Identify and predict disease and personalize recommendations in healthcare • Optimize shipping routes in real time for transportation • Accurately evaluate athletes’ performance in sports • Prevent tax evasion and predict incarceration rates for governments • Automate digital ad placement in e-commerce • Improve online experiences for gaming • Create algorithms to pinpoint compatible partners for social media Source: 22 Data Science Applications and Examples, Built In, by Mae Rice and updated by Jessica Powers, June 2022 Explore examples of how companies are applying data science to improve and solve global problems. Expand each section to study applications of data science. Helpful
• Salvador Henriques Nunda @salvadornunda 1 month ago Amei demais este curso aprendi bastante eu tive o basico sobre programação e com este curso aprondei o meu conhecimento sobre programsção Helpful
• Arpit Raj 1 year ago Title: CS50's Introduction to Computer Science: A Remarkable Journey into the World of Computing CS50's Introduction to Computer Science is an exceptional online course that delivers a comprehensive and engaging experience for anyone interested in… Read more Title: CS50's Introduction to Computer Science: A Remarkable Journey into the World of Computing CS50's Introduction to Computer Science is an exceptional online course that delivers a comprehensive and engaging experience for anyone interested in exploring the vast realm of computer science. Developed by the renowned Harvard University, this course serves as a perfect starting point for beginners and a valuable resource for those seeking to deepen their understanding of computer science concepts. One of the most remarkable aspects of CS50 is its ability to cater to a wide range of learners, regardless of their prior knowledge or experience. The course is designed in a way that ensures accessibility for beginners, without compromising on the quality or depth of the material covered. It effectively breaks down complex concepts into easily digestible modules, making it suitable for individuals with various learning styles. The course content is incredibly comprehensive, covering fundamental topics such as algorithms, data structures, programming languages (including C, Python, and JavaScript), web development, and even aspects of artificial intelligence. Each topic is presented through a combination of lectures, problem sets, and practical assignments that allow students to apply their knowledge in real-world scenarios. The course strikes a perfect balance between theory and hands-on practice, ensuring a solid foundation in computer science principles. David J. Malan, the course instructor, deserves immense praise for his exceptional teaching style. His enthusiasm, clarity, and ability to communicate complex ideas in a relatable manner make the lectures highly engaging. The course also benefits from a supportive online community, including discussion forums and a dedicated Slack channel, where students can seek help, collaborate, and engage with fellow learners from all over the world. CS50's Introduction to Computer Science also emphasizes critical thinking and problem-solving skills, which are invaluable in the field of computer science. The problem sets and assignments challenge students to think creatively and develop efficient algorithms to tackle real-world problems. This approach not only enhances programming skills but also fosters a mindset that is crucial in any technological discipline. Furthermore, the course provides excellent resources for self-assessment and evaluation. Regular quizzes and problem sets allow students to gauge their understanding of the material and identify areas that require further attention. Additionally, the course's grading system provides a sense of accomplishment and motivation to progress further. In conclusion, CS50's Introduction to Computer Science is an exceptional online course that excels in its ability to deliver a comprehensive and engaging learning experience. Whether you're a beginner exploring the world of computer science or a seasoned professional looking to refresh your knowledge, this course will undoubtedly meet and exceed your expectations. The combination of high-quality content, exceptional instruction, and a supportive online community makes CS50 a must-take course for anyone interested in computer science. Helpful
• Vaibhav Pratap Singh Kushwah @CourseExplorerX 5 months ago Harvard's CS50 is a comprehensive introduction to computer science, covering a wide array of topics from programming fundamentals to more advanced concepts like algorithms and data structures. The course offers engaging lectures, problem sets, and p… Read more Harvard's CS50 is a comprehensive introduction to computer science, covering a wide array of topics from programming fundamentals to more advanced concepts like algorithms and data structures. The course offers engaging lectures, problem sets, and projects that challenge students to think critically and creatively. Through hands-on assignments, students gain practical coding skills in languages like C, Python, and JavaScript. The course's supportive online community and extensive resources, including discussion forums and office hours, foster collaboration and learning. However, some learners may find the pace and difficulty of the course demanding, especially without prior programming experience. Overall, CS50 provides a rigorous foundation in computer science, making it an excellent choice for individuals seeking a thorough understanding of the field. Helpful
• Jo H @yohannis 11 months ago Harvard CS50 is an exceptional computer science course that offers a comprehensive and engaging learning experience. The course covers a wide range of topics, from programming basics to more advanced concepts, providing a solid foundation for anyone interested in computer science. The interactive lectures, challenging problem sets, and supportive community make learning enjoyable. The course's emphasis on problem-solving and critical thinking prepares students for real-world challenges. Overall, Harvard CS50 is an excellent choice for both beginners and those seeking to deepen their understanding of computer science. Helpful
• Abdulai Jabaty 4 months ago Introduction: I recently completed an online computer science course, and I would like to share my thoughts and experiences with you. This comprehensive course covered a wide range of topics and provided a solid foundation in computer science princi… Read more Introduction: I recently completed an online computer science course, and I would like to share my thoughts and experiences with you. This comprehensive course covered a wide range of topics and provided a solid foundation in computer science principles and practical skills. Here is my review of the course. Course Content and Structure: The course was well-structured, with each module building upon the previous one. It covered essential topics such as programming languages, algorithms, data structures, computer architecture, software engineering, and databases. The content was presented in a clear and organized manner, making it easy to follow along and understand complex concepts. Instructors and Teaching Methods: The instructors were knowledgeable and experienced in the field of computer science. They effectively conveyed their expertise through well-prepared lectures, engaging demonstrations, and practical examples. The teaching methods employed, including interactive quizzes, coding exercises, and real-world case studies, enhanced the learning experience and helped solidify the concepts taught. Hands-on Learning Opportunities: One of the highlights of this course was the emphasis on hands-on learning. Throughout the course, I had numerous opportunities to apply the theoretical knowledge gained to practical coding exercises and projects. This hands-on approach allowed me to develop my programming skills and gain confidence in implementing algorithms and solving real-world problems. Support and Community: The course provided excellent support to students. The instructors were responsive to questions and provided timely feedback on assignments. Additionally, there was an active online community where students could interact, collaborate, and seek help from their peers. This sense of community fostered a supportive learning environment and encouraged knowledge sharing. Resources and Materials: The course materials were comprehensive and well-curated. The instructors provided lecture notes, supplementary readings, and additional resources to further explore the topics covered. The availability of these resources allowed me to delve deeper into specific areas of interest and broaden my understanding of computer science beyond the course curriculum. Conclusion: Overall, the online computer science course I completed was a valuable learning experience. It provided a solid foundation in computer science principles, practical skills, and problem-solving techniques. The well-structured content, knowledgeable instructors, hands-on learning opportunities, supportive community, and comprehensive resources all contributed to a positive and enriching learning journey. I highly recommend this course to anyone interested in gaining a strong understanding of computer science concepts and applications. Helpful
• DEVENDRA TAMBE 9 months ago Harvard's CS50 is a comprehensive and renowned computer science course that provides a deep dive into various aspects of the field. Here's a review of the course: Content: CS50 covers a wide range of computer science topics, from the fundamentals o… Read more Harvard's CS50 is a comprehensive and renowned computer science course that provides a deep dive into various aspects of the field. Here's a review of the course: Content: CS50 covers a wide range of computer science topics, from the fundamentals of programming to more advanced concepts like algorithms, data structures, and web development. The course also delves into computer security and explores languages like C, Python, and SQL. The breadth of material ensures a well-rounded understanding of computer science. Quality of Instruction: Professor David Malan's engaging and charismatic teaching style is a highlight of the course. His lectures are clear, and he breaks down complex concepts into understandable components. The course materials, including problem sets, are of high quality and challenge students to apply what they've learned. Problem Sets: CS50 is known for its rigorous problem sets, which encourage hands-on learning. These assignments can be challenging, but they offer a great opportunity to practice and apply the knowledge gained in the lectures. They are well-structured and progressively build on each other. Community and Support: The course has a strong online community through forums and support from teaching assistants. Students can get help when they're stuck on a problem set or need clarification on a concept. This community aspect adds to the overall learning experience. Accessibility: CS50 is available online for free, making it accessible to anyone with an internet connection. It's a great resource for self-learners and those looking to explore computer science without the need for formal enrollment at Harvard. Flexibility: CS50 can be taken at your own pace. While the course is typically offered over a semester, you can work through the materials and assignments on your own schedule, which is ideal for those with busy lives or other commitments. Challenges: The difficulty of CS50 can be a double-edged sword. While it's a fantastic course, some may find it quite demanding, especially if they are new to programming. It requires dedication and time. In summary, Harvard's CS50 is a top-notch computer science course with a rich curriculum, excellent instruction, and a strong community. Whether you're a beginner or looking to deepen your knowledge in computer science, this course is a valuable resource. It's a challenging but rewarding journey that can equip you with valuable skills in the world of programming and computer science. Helpful
• Md Alif Hossain @REXX 1 year ago Harvard CS50 – Full Computer Science University Course is an incredibly comprehensive course that covers all aspects of computer science and programming. The course dives deep into topics like data structures and algorithms, computer systems and net… Read more Harvard CS50 – Full Computer Science University Course is an incredibly comprehensive course that covers all aspects of computer science and programming. The course dives deep into topics like data structures and algorithms, computer systems and networking, web development, software engineering, and a lot more. The course is taught by experienced Harvard professors and includes engaging lectures and hands-on programming labs. The course is also very well organized and easy to follow, which makes understanding complex topics much easier. The course also provides a great foundation for future computer science courses or even a career in programming. All in all, Harvard CS50 – Full Computer Science University Course is a great course and I highly recommend it to anyone who is looking to get started in the field of computer science. Helpful
• EM EKTA MITTAL 4 months ago One of the most remarkable aspects of CS50 is its ability to cater to a wide range of learners, regardless of their prior knowledge or experience. The course is designed in a way that ensures accessibility for beginners, without compromising on the… Read more One of the most remarkable aspects of CS50 is its ability to cater to a wide range of learners, regardless of their prior knowledge or experience. The course is designed in a way that ensures accessibility for beginners, without compromising on the quality or depth of the material covered. It effectively breaks down complex concepts into easily digestible modules, making it suitable for individuals with various learning styles. The course content is incredibly comprehensive, covering fundamental topics such as algorithms, data structures, programming languages (including C, Python, and JavaScript), web development, and even aspects of artificial intelligence. Each topic is presented through a combination of lectures, problem sets, and practical assignments that allow students to apply their knowledge in real-world scenarios. The course strikes a perfect balance between theory and hands-on practice, ensuring a solid foundation in computer science principles. David J. Malan, the course instructor, deserves immense praise for his exceptional teaching style. His enthusiasm, clarity, and ability to communicate complex ideas in a relatable manner make the lectures highly engaging. The course also benefits from a supportive online community, including discussion forums and a dedicated Slack channel, where students can seek help, collaborate, and engage with fellow learners from all over the world. CS50's Introduction to Computer Science also emphasizes critical thinking and problem-solving skills, which are invaluable in the field of computer science. The problem sets and assignments challenge students to think creatively and develop efficient algorithms to tackle real-world problems. This approach not only enhances programming skills but also fosters a mindset that is crucial in any technological discipline. Furthermore, the course provides excellent resources for self-assessment and evaluation. Regular quizzes and problem sets allow students to gauge their understanding of the material and identify areas that require further attention. Additionally, the course's grading system provides a sense of accomplishment and motivation to progress further. In conclusion, CS50's Introduction to Computer Science is an exceptional online course that excels in its ability to deliver a comprehensive and engaging learning experience. Whether you're a beginner exploring the world of computer science or a seasoned professional looking to refresh your knowledge, this course will undoubtedly meet and exceed your expectations. The combination of high-quality content, exceptional instruction, and a supportive online community makes CS50 a must-take course for anyone interested in computer science. Helpful
• Jenil Dholakiya 1 year ago I recently had the privilege of enrolling in the Harvard CS50 course, a comprehensive and in-depth computer science university course. As an aspiring programmer, I can confidently say that this course has been a transformative experience for me. Fr… Read more I recently had the privilege of enrolling in the Harvard CS50 course, a comprehensive and in-depth computer science university course. As an aspiring programmer, I can confidently say that this course has been a transformative experience for me. From the very beginning, the CS50 course captured my attention with its engaging lectures, challenging problem sets, and practical hands-on projects. The teaching staff, led by the brilliant Professor David J. Malan, demonstrated a remarkable ability to simplify complex concepts and deliver them in a way that was accessible to both beginners and experienced programmers. One of the standout features of CS50 is its emphasis on a wide range of programming languages and technologies. It covers the fundamentals of C, Python, JavaScript, and even delves into web development, databases, and mobile app development. This multidisciplinary approach gives students a holistic understanding of computer science and equips them with the skills to tackle real-world challenges. The problem sets and projects offered throughout the course were a perfect blend of theory and practice. They were thoughtfully designed to reinforce the concepts taught in the lectures while encouraging creativity and problem-solving skills. The CS50 staff also fostered a supportive community through online forums, where students could seek help, collaborate, and share ideas. The sense of camaraderie and encouragement made the learning experience even more rewarding. Another remarkable aspect of CS50 is the high production value of its video lectures. The course materials are beautifully produced, with visually appealing graphics and animations that enhance the learning experience. The lecture videos are supplemented with comprehensive slides and transcripts, making it easy to revisit topics or catch up if needed. Furthermore, CS50 goes beyond just teaching programming languages; it instills crucial problem-solving techniques, critical thinking, and a deep understanding of algorithms and data structures. These skills are invaluable in any technology-related field and have far-reaching applications beyond the course itself. In conclusion, Harvard CS50 is an exceptional computer science course that leaves a lasting impact on students. It provides an all-encompassing introduction to the world of computer science, equipping learners with a solid foundation and the ability to tackle complex programming challenges. The expertise and dedication of the teaching staff, the engaging problem sets, and the supportive community fostered throughout the course make it a standout choice for anyone interested in embarking on a journey into the world of computer science. I highly recommend Harvard CS50 to aspiring programmers and anyone seeking to expand their knowledge and skills in the field of computer science. Helpful
• OP OjoAbbey Olalekan Paul 1 year ago I recently had the privilege of enrolling in the Harvard CS50 course, a comprehensive and in-depth computer science university course. As an aspiring programmer, I can confidently say that this course has been a transformative experience for me. Fr… Read more I recently had the privilege of enrolling in the Harvard CS50 course, a comprehensive and in-depth computer science university course. As an aspiring programmer, I can confidently say that this course has been a transformative experience for me. From the very beginning, the CS50 course captured my attention with its engaging lectures, challenging problem sets, and practical hands-on projects. The teaching staff, led by the brilliant Professor David J. Malan, demonstrated a remarkable ability to simplify complex concepts and deliver them in a way that was accessible to both beginners and experienced programmers. One of the standout features of CS50 is its emphasis on a wide range of programming languages and technologies. It covers the fundamentals of C, Python, JavaScript, and even delves into web development, databases, and mobile app development. This multidisciplinary approach gives students a holistic understanding of computer science and equips them with the skills to tackle real-world challenges. The problem sets and projects offered throughout the course were a perfect blend of theory and practice. They were thoughtfully designed to reinforce the concepts taught in the lectures while encouraging creativity and problem-solving skills. The CS50 staff also fostered a supportive community through online forums, where students could seek help, collaborate, and share ideas. The sense of camaraderie and encouragement made the learning experience even more rewarding. Another remarkable aspect of CS50 is the high production value of its video lectures. The course materials are beautifully produced, with visually appealing graphics and animations that enhance the learning experience. The lecture videos are supplemented with comprehensive slides and transcripts, making it easy to revisit topics or catch up if needed. Furthermore, CS50 goes beyond just teaching programming languages; it instills crucial problem-solving techniques, critical thinking, and a deep understanding of algorithms and data structures. These skills are invaluable in any technology-related field and have far-reaching applications beyond the course itself. In conclusion, Harvard CS50 is an exceptional computer science course that leaves a lasting impact on students. It provides an all-encompassing introduction to the world of computer science, equipping learners with a solid foundation and the ability to tackle complex programming challenges. The expertise and dedication of the teaching staff, the engaging problem sets, and the supportive community fostered throughout the course make it a standout choice for anyone interested in embarking on a journey into the world of computer science. I highly recommend Harvard CS50 to aspiring programmers and anyone seeking to expand their knowledge and skills in the field of computer science. Helpful

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Course Listing

For a snapshot of courses being offered by Harvard School of Engineering over the next four years, visit our  Multi Year Course Planning  tool.

Great Ideas in Computer Science

COMPSCI 1 2025 Spring

Henry Leitner Tuesday, Thursday 10:30am to 11:45am

An introduction to the most important discoveries and intellectual paradigms in computer science, designed for students with little or no previous background. Explores problem-solving and data analysis using Python, a programming language with a simple syntax and a powerful set of libraries. This course covers basic data types and collections (lists, dictionaries, tuples, and sets), control flow, recursion, supervised machine learning via regression, visualization, information hiding and encapsulation using classes and objects, and introduces the analysis of program performance. Presents an integrated view of computer systems, from switching circuits up through compilers, and examines theoretical and practical limitations related to unsolvable and intractable computational problems. Other topics include the social and ethical dilemmas presented by such issues as software unreliability, algorithmic bias, and invasions of privacy.

Discrete Mathematics for Computer Science

COMPSCI 20 2024 Fall

Michael Mitzenmacher, Kitty Ascrizzi Monday, Wednesday, Friday 9:45am to 11:00am

Widely applicable mathematical tools for computer science, including topics from logic, set theory, combinatorics, number theory, probability theory, and graph theory. Practice in reasoning formally and proving theorems.

COMPSCI 20 2025 Spring

Rebecca Nesson, Adam Hesterberg Monday, Wednesday, Friday 9:45am to 11:00am

Computational Thinking and Problem Solving

COMPSCI 32 2025 Spring

Michael Smith, Kitty Ascrizzi Monday, Wednesday 12:00pm to 1:15pm

An introduction to computational thinking, useful concepts in the field of computer science, and the art of computer programming using Python. Significant emphasis is placed on class meetings and learning to use computers to solve complex, real-world problems. Concepts and techniques are introduced as they are needed to help solve the problems confronting us. Students will learn how to go from an ambiguous problem description to a running solution and will leave the class knowing how to instruct computers to do what they want them to do. Prior experience in computer science or computer programming is not necessary.

Incentives in the Wild: from Tanking in Sports to Mining Cryptocurrencies

COMPSCI 37 2025 Spring

Yannai Gonczarowski Monday, Wednesday 1:30pm to 2:45pm

How could it be that paving a new road might increase congestion for all drivers? Why would a professional sports team ever try not to score in a game that it wants to win? Why would any student rank high schools not in their order of preference when applying? And what are some incentive pitfalls that the designer of a cryptocurrency system should be aware of? In this course, we will examine seemingly strange social phenomena, use mathematical tools to model them and to analyze how and why distorted incentives give rise to them, and explore potential mechanisms to eliminate such phenomena.

Introduction to Computer Science

COMPSCI 50 2024 Fall

David J. Malan Monday 1:30pm to 4:15pm

This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (More than half of CS50 students have never taken CS before!) This course teaches you how to solve problems, both with and without code, with an emphasis on correctness, design, and style. Topics include computational thinking, abstraction, algorithms, data structures, and computer science more generally. Problem sets inspired by the arts, humanities, social sciences, and sciences. More than teach you how to program in one language, this course teaches you how to program fundamentally and how to teach yourself new languages ultimately. The course starts with a traditional but omnipresent language called C that underlies today's newer languages, via which you'll learn not only about functions, variables, conditionals, loops, and more, but also about how computers themselves work underneath the hood, memory and all. The course then transitions to Python, a higher-level language that you'll understand all the more because of C. Toward term's end, the course introduces SQL, via which you can store data in databases, along with HTML, CSS, and JavaScript, via which you can create web and mobile apps alike. Course culminates in a final project. See https://cs50.harvard.edu/college for advice, FAQs, syllabus, and what's new. Email the course's heads at [email protected] with questions.

Introduction to Computer Science (for students unable to take in fall term)

COMPSCI 50 2025 Spring

David J. Malan, Yuliia Zhukovets Tuesday 9:00am to 11:45am

David J. Malan, Yuliia Zhukovets Wednesday 9:00am to 11:45am

David J. Malan, Yuliia Zhukovets Tuesday 3:45pm to 6:30pm

David J. Malan, Yuliia Zhukovets Wednesday 6:00pm to 8:45pm

Abstraction and Design in Computation

COMPSCI 51 2025 Spring

Stuart Shieber Tuesday, Thursday 12:45pm to 2:00pm

Fundamental concepts in the design of computer programs, emphasizing the crucial role of abstraction. The goal of the course is to give students insight into the difference between programming and programming well. To emphasize the differing approaches to expressing programming solutions, you will learn to program in a variety of paradigms -- including functional, imperative, and object-oriented. Important ideas from software engineering and models of computation will inform these different views of programming.

Stuart Shieber

Stuart Shieber Tuesday, Thursday 3:45pm to 5:00pm

Systems Programming and Machine Organization

COMPSCI 61 2024 Fall

Eddie Kohler Monday, Wednesday 2:15pm to 3:30pm

Fundamentals of computer systems programming, machine organization, and performance tuning. This course provides a solid background in systems programming and a deep understanding of low-level machine organization and design. Topics include C and assembly language programming, program optimization, memory hierarchy and caching, virtual memory and dynamic memory management, concurrency, threads, and synchronization.

Code, Data, and Art

COMPSCI 73 2024 Fall

Fernanda Viegas, Martin Wattenberg Tuesday, Thursday 11:15am to 12:30pm

A studio course where software is used as an artistic medium. The course is designed to expose students to current perspectives on the intersection of computer science and art, and to build skills that will allow them to express themselves creatively via software. An additional focus will be the role of data in modern artistic practice.

Supervised Reading and Research

COMPSCI 91R 2024 Fall

Eddie Kohler, Adam Hesterberg

Supervised individual study of advanced topics in computer science. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. Additional information and a form are available via https://harvardcs.info/forms/#cs-91r-form . The form must be filled out and signed by the student and faculty supervisor. Students writing theses may enroll in this course while conducting thesis research and writing.

COMPSCI 91R 2025 Spring

Boaz Barak, Stephen Chong, Adam Hesterberg

Privacy and Technology

COMPSCI 1050 2024 Fall

Jim Waldo Tuesday, Thursday 12:45pm to 2:00pm

What is privacy, and how is it affected by recent developments in technology? This course critically examines popular concepts of privacy and uses a rigorous analysis of technologies to understand the policy and ethical issues at play. Case studies: database anonymity, research ethics, wiretapping, surveillance, and others. Course relies on some technical material, but is open and accessible to all students, especially those with interest in economics, engineering, political science, computer science, sociology, biology, law, government, philosophy.

Systems Development for Computational Science

COMPSCI 1070 2024 Fall

This is a project-based course emphasizing designing, building, testing, maintaining, and modifying software for scientific computing and data sciences. The class is focusing on a thorough introduction of the Python programming language with discussion of core concepts in object-oriented programming as well as essential data structures useful in most programming tasks. Students will work in groups on a semester long project. Students will further learn how to work with SQL databases and how to integrate them in Python using SQLite3 and Pandas. After completion of this course, students will be able to adapt basic tools and techniques to design complex software systems aimed at solving computational and data processing problems in academic and industrial environments.

Data Science 1: Introduction to Data Science

COMPSCI 1090A 2024 Fall

Pavlos Protopapas, Natesh Pillai Monday, Wednesday, Friday 9:00am to 10:15am

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set;  (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication – summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year.

Data Science 2: Advanced Topics in Data Science

COMPSCI 1090B 2025 Spring

Pavlos Protopapas, Natesh Pillai Monday, Wednesday, Friday 9:45am to 11:00am

Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, and unsupervised learning. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part two of a two-part series.

Introduction to Algorithms and their Limitations

COMPSCI 1200 2024 Fall

Anurag Anshu, Salil Vadhan Tuesday, Thursday 9:45am to 11:00am

An introductory course in theoretical computer science, aimed at giving students the power of using mathematical abstraction and rigorous proof to understand computation. Thus equipped, students will be able to design and use algorithms that apply to a wide variety of computational problems, with confidence about their correctness and efficiency, as well as recognize when a problem may have no algorithmic solution. At the same time, they will gain an appreciation for the beautiful mathematical theory of computation that is independent of (indeed, predates) the technology on which it is implemented.

Introduction to Theoretical Computer Science

COMPSCI 1210 2024 Fall

Adam Hesterberg Tuesday, Thursday 3:45pm to 5:00pm

Computation occurs over a variety of substrates including silicon, neurons, DNA, the stock market, bee colonies and many others. In this course we will study the fundamental capabilities and limitations of computation, including the phenomenon of universality and the duality of code and data. Some of the questions we will touch upon include: Are there functions that cannot be computed? Are there true mathematical statements that can't be proven? Are there encryption schemes that can't be broken? Is randomness ever useful for computing? Can we use the quirks of quantum mechanics to speed up computation?

Data Structures and Algorithms

COMPSCI 1240 2025 Spring

Madhu Sudan, Sitan Chen Monday, Wednesday 2:15pm to 3:30pm

Design and analysis of efficient algorithms and data structures. Algorithm design methods, graph algorithms, approximation algorithms, and randomized algorithms are covered.

Fairness and Privacy: Perspectives from Law and Probability

COMPSCI 1260 2024 Fall

Cynthia Dwork Monday 9:00am to 10:15am

Algorithms are mathematical objects with real life consequences. How do you say “fairness” and “privacy” in mathematics?  How do existing theoretical computer science formulations mesh with legal privacy and nondiscrimination notions? Drawing on key concepts from differential privacy, the theory of algorithmic fairness, and crytography, the course focuses on the analysis and mitigation of privacy loss and unfairness in machine learning and data analysis. Through joint readings and weekly class meetings with the HLS course of the same name, students will develop disciplinary “bilingualism.”

Cryptography

COMPSCI 1270 2025 Spring

Boaz Barak Monday, Wednesday 12:45pm to 2:00pm

Cryptography is as old as human communication itself, but has undergone a revolution in the last few decades. It is now about much more than "secret writing" and includes seemingly paradoxical notions such as communicating securely without a shared secret, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as fully homomorphic encryption and software obfuscation. This is a proof-based course that will be best appreciated by mathematically mature students.

Economics and Computation

COMPSCI 1360 2025 Spring

Ariel Procaccia Monday, Wednesday 9:45am to 11:00am

The course examines the interplay between economic thinking and computational thinking as it relates to the design of online platforms and societal decision-making mechanisms. The focus is on fundamental concepts, modeling, and mathematical analysis. Topics covered include game theory, incentive alignment, matching, social choice, fair division and social networks. Special attention is given to ideas that draw on both disciplines, such as worst-case bounds on the inefficiency of equilibria, voting rules that are computationally hard to manipulate, and approximation algorithms that discourage strategic behavior.  

Computing Hardware

COMPSCI 1410 2024 Fall

Woodward Yang Monday, Wednesday 12:45pm to 2:00pm

This course delves into the design principles and practices of high performance digital computing systems that are cost effectively and reliably manufactured with billions of near atomic scale semiconductor components. Key abstractions and foundational concepts are emphasized as the course covers the basic operation of CMOS transistors and logic gates, combinational and sequential logic including Finite State Machines (FSMs), digital memory subsystems, and machine code culminating with the implementation of a MIPS processor. Lab assignments will focus on the practical aspects of digital hardware design by utilizing Field Programmable Gate Arrays (FPGAs), Verliog (Hardware Description Language) and advanced CAD tools for the design, simulation and verification of digital computing hardware.

Computer Networks

COMPSCI 1430 2025 Spring

H. Kung Monday, Wednesday 3:45pm to 5:00pm

Computer networking has enabled the emergence of mobile and cloud computing, creating two of the most significant technological breakthroughs in computing. Computer networks have become even more critical these days since remote activities have become a new norm. We expect several focuses in the coming years. First, we will witness the emergence of 5G wireless mobile networks, which have already begun to replace the current 4G networks. Second, cybersecurity and privacy will receive unprecedented attention from the industry. Third, blockchain technology, which underlies Bitcoin, creates a new trusted network infrastructure for many new distributed applications. Fourth, distance learning and virtual meetings will push the limits of current multicast and network management technologies. In this course, students will learn basic networking protocols as well as these timely topics.

Networking at Scale

COMPSCI 1450 2025 Spring

Minlan Yu Tuesday, Thursday 11:15am to 12:30pm

This course studies computer network topics including Layer 2/Layer 3 topology, routing, transport protocols, traffic engineering, network functions, programmable switches, and software-defined networking. Modern networks have grown to large scale (connecting millions of servers) and high speed (terabits per second) to meet the needs of cloud applications in business and society. Thus, in addition to learning the conventional concepts in networking, we will also discuss how to adapt these concepts to large-scale networks. These discussions will hopefully help deepen our understanding of networking technologies. This course includes lectures and system programming projects. More information can be found at https://github.com/minlanyu/cs145-site .

Programming Languages

COMPSCI 1520 2025 Spring

Nada Amin Tuesday, Thursday 11:15am to 12:30pm

Comprehensive introduction to the principal features and overall design of both traditional and modern programming languages, including syntax, formal semantics, abstraction mechanisms, modularity, type systems, naming, polymorphism, closures, continuations, and concurrency. Provides the intellectual tools needed to design, evaluate, choose, and use programming languages.

Operating Systems

COMPSCI 1610 2025 Spring

James Mickens Monday, Wednesday 2:15pm to 3:30pm

This course focuses on the design and implementation of modern operating systems. The course discusses threads, processes, virtual memory, schedulers, and the other fundamental primitives that an OS uses to represent active computations. An exploration of the system call interface explains how applications interact with hardware and other programs which are concurrently executing. Case studies of popular file systems reveal how an OS makes IO efficient and robust in the midst of crashes and unexpected reboots. Students also learn how virtualization allows a physical machine to partition its resources across multiple virtual machines. Class topics are reinforced through a series of intensive programming assignments which use a real operating system.

Data Systems

COMPSCI 1650 2024 Fall

Stratos Idreos Tuesday, Thursday 9:45am to 11:00am

We are in the big data era and data systems sit in the critical path of everything we do. We are going through major transformations in businesses, sciences, as well as everyday life - collecting and analyzing data changes everything and data systems provide the means to store and analyze a massive amount of data. This course is a comprehensive introduction to modern data systems. The primary focus of the course is on the modern trends that are shaping the data management industry right now: column-store and hybrid systems, shared nothing architectures, cache conscious algorithms, hardware/software co-design, main-memory systems, adaptive indexing, stream processing, scientific data management, and key-value stores. We also study the history of data systems, traditional and seminal concepts and ideas such as the relational model, row-store database systems, optimization, indexing, concurrency control, recovery and SQL. In this way, we discuss both how and why data systems evolved over the years, as well as how these concepts apply today and how data systems might evolve in the future. We focus on understanding concepts and trends rather than specific techniques that will soon be outdated - as such the class relies largely on recent research material and on a semi-flipped class model with a lot of hands-on interaction in each class.

Visualization

COMPSCI 1710 2024 Fall

Johannes Knittel Monday, Wednesday 2:15pm to 3:30pm

An introduction to key design principles and techniques for visualizing data. Covers design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Introduces programming of web-based interactive visualizations.

Engineering Usable Interactive Systems

COMPSCI 1780 2025 Spring

Monday, Wednesday 3:45pm to 5:00pm

In this course, students learn critical techniques, concepts, and technologies for building usable interactive systems, alone and in pairs. Assignments provide hands-on experiences with different modern frameworks, platforms, and libraries while conceptual commonalities and distinctions are annotated and explained. Lectures cover relevant basic and advanced topics, such as human cognitive capabilities, iterative prototyping, and human-AI interaction. The final project will require both front-end and back-end development, iterative prototyping with humans, and a final evaluation with target users. Designed for advanced undergraduates.

Machine Learning

COMPSCI 1810 2025 Spring

Finale Doshi-Velez, David Alvarez Melis, Stephanie Gil Tuesday, Thursday 9:45am to 11:00am

Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.

Planning and Learning Methods in AI

COMPSCI 1820 2025 Spring

Stephanie Gil

Artificial Intelligence (AI) is already making a powerful impact on modern technology, and is expected to be even more transformative in the near future. The course introduces the ideas and techniques underlying this exciting field, with the goal of teaching students to identify effective representations and approaches for a wide variety of computational tasks. Topics covered in this course are broadly divided into search and planning, optimization and games, and uncertainty and learning. Special attention is given to ethical considerations in AI and to applications that benefit society. For more information please see the course website .

Introduction to Reinforcement Learning

COMPSCI 1840 2024 Fall

Lucas Janson Monday, Wednesday 10:30am to 11:45am

Modern AI systems often need the ability to make sequential decisions in an unknown, uncertain, possibly hostile environment, by actively interacting with the environment to collect relevant data. Reinforcement Learning (RL) is a general framework that can capture the interactive learning setting and has been used to design intelligent agents that achieve high-level performance in challenging applications such as Go, computer games, robotic manipulation, health care, and education.

This course provides an introduction to reinforcement learning covering a range of problem formulations, algorithms, and theory. The four main themes of the course are (1) Markov decision processes (Bellman equations/optimality, planning, UCB, unknown environments, linear quadratic control, exploration, imitation learning), (2) bandits (epsilon-greedy, UCB, Thompson sampling, contextual bandits, linear bandits, exploration in MDPs), and (3) methods for large-scale systems (policy gradient methods, deep RL, Monte Carlo tree search, Q-learning). There will also be an Embedded Ethics lecture on ethical issues arising in reinforcement learning. The assignments will focus on a mix of algorithmic and statistical principles, along with their programming implementations.

Introduction to Computational Linguistics and Natural-language Processing

COMPSCI 1870 2024 Fall

Stuart Shieber Monday, Wednesday, Friday 11:15am to 12:30pm

Natural-language-processing applications are ubiquitous: Alexa can set a reminder, or play a particular song, or provide your local weather if you ask; Google Translate can make documents readable across languages; ChatGPT can be prompted to generate convincingly fluent text, which is often even correct. How do such systems work? This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as these. You will work with ideas from linguistics, statistical modeling, machine learning, and neural networks, with emphasis on their application, limitations, and implications. The course is lab- and project-based, primarily in small teams, and culminates in the building and testing of a question-answering system.

Classics of Computer Science

COMPSCI 1910 2025 Spring

Harry Lewis Tuesday, Thursday 2:15pm to 3:30pm

Papers every computer scientist should have read, from all areas of the field and dating from its origins to the present.

Designing K–12 Computer Science Learning Experiences

COMPSCI 1960 2025 Spring

Karen Brennan Wednesday 9:00am to 11:45am

From computational thinking to workforce arguments, there is considerable interest in and excitement about including computer science education for all K–12 students. Yet, unlike other disciplines with a much longer history in formal schooling, the interest in computer science education is not yet supported by commensurate attention to research and teacher practice. In this course, we will examine the state of K–12 computing education: questioning its value, examining its history, and imagining and contributing to its potential. The course will be organized as both a reading group and a lab, building a community of people who are committed to K–12 CS education. Each week you will read classic and current research, and write accompanying memos to document your evolving understandings of the field. Throughout the course, either individually or with partners, you will develop an independent project that explores the design of K–12 computer science learning experiences. Some examples of possible projects include: designing CS-standalone or cross-curricular learning activities and curriculum, building a programming language for novices, developing an annotated bibliography, critically analyzing policy documents such as curriculum frameworks and standards from around the world, or contributing to current K–12 CS education research initiatives.

High Performance Computing for Science and Engineering

COMPSCI 2050 2025 Spring

Ignacio Becker Troncoso Tuesday, Thursday 2:15pm to 3:30pm

With manufacturing processes reaching the limits in terms of transistor density on today’s computing architectures, efficient modern code must exploit parallel execution to maintain scaling of available hardware resources. The use of computers in academia, industry and society is a fundamental tool for solving (scientific) problems while the "think parallel" mindset of code developers is still lagging behind. The aim of this course is to introduce the student to the fundamentals of parallel programming and its relationship on computer architectures. Various forms of parallelism are discussed and exploited through different programming models with focus on shared and distributed memory programming. The learned techniques are tried out by means of homework, lab sessions and a term project.

Applied Privacy for Data Science

COMPSCI 2080 2025 Spring

Salil Vadhan Monday, Wednesday 11:15am to 12:30pm

The risks to privacy when making human subjects data available for research and how to protect against these risks using the formal framework of differential privacy. Methods for attacking statistical data releases, the mathematics of and software implementations of differential privacy, deployed solutions in industry and government. Assignments will include implementation and experimentation on data science tasks.

Computational Complexity

COMPSCI 2210 2024 Fall

Madhu Sudan Monday, Wednesday 11:15am to 12:30pm

A quantitative theory of the resources needed for computing and the impediments to efficient computation. The models of computation considered include ones that are finite or infinite, deterministic, randomized, quantum or nondeterministic, discrete or algebraic, sequential or parallel.

Algorithms at the Ends of the Wire

COMPSCI 2241 2025 Spring

Michael Mitzenmacher Tuesday, Thursday 11:15am to 12:30pm

Covers topics related to algorithms for big data, especially related to networks and database systems. Themes include sketch-based data structures, compression, graph and link information, and information theory. Requires a major final research-based project.

Algorithms for Data Science

COMPSCI 2243 2024 Fall

Sitan Chen Monday, Wednesday 2:15pm to 3:30pm

This is a graduate topics class on algorithmic challenges in modern machine learning and data science. We will touch upon a number of domains (generative modeling, deep learning theory, robust statistics, Bayesian inference) and frameworks for algorithm design (spectral/tensor methods, moment methods, message passing, diffusions), focusing on provable guarantees. The theory draws upon a range of techniques from stochastic calculus, harmonic analysis, statistical physics, algebra, and beyond. We will also explore the myriad modeling challenges in building this theory and prominent paradigms (semi-random models, smoothed complexity, oracles) for going beyond traditional worst-case analysis.

COMPSCI 2270 2025 Spring

Computational Learning Theory

COMPSCI 2280 2025 Spring

Leslie Valiant Tuesday, Thursday 12:45pm to 2:00pm

Possibilities of and limitations to performing learning by a computational process. Computationally feasible generalization and its limits. Topics include computational models of learning, polynomial time learnability, learning from examples and from queries to oracles. Applications to Boolean functions, languages and geometric functions. Darwinian evolution as learning.

Topics in Foundations of ML: Mathematical & Engineering Principles for Training Foundation Models

COMPSCI 2281R 2024 Fall

Sham Kakade Thursday 3:45pm to 6:30pm

This will be a graduate level course on recent advances and open questions in the foundations of machine learning and specifically deep learning. We will review both classical results as well as recent papers in areas including classifiers and generalization gaps, representation learning, generative models, adversarial robustness, out of distribution performance, and more.

This is a fast-moving area and it will be a fast-moving course. We will aim to cover both state-of-art results, as well as the intellectual foundations for them, and have a substantive discussion on both the “big picture” and technical details of the papers. In addition to the theoretical lectures, the course will involve a programming component aiming to get students to the point where they can both reproduce results from papers and work on their own research. This component will be largely self-directed and we expect students to be proficient in Python and in picking up technologies and libraries such as pytorch/numpy/etc on their own (aka “Stack Overflow oriented programming”).

Economic Analysis as a Frontier of Theoretical Computer Science

COMPSCI 2370 2024 Fall

How can we use tools from statistical learning theory to design better auctions? Can we use cryptography to better implement matching mechanisms? And how should we approach formally proving that welfare in Nash equilibria for many games is not "much worse" than in the social optimum? This course explores the application of diverse ideas, techniques, and solution aesthetics from theoretical computer science to derive meaningful new insights into classic economic problems. The three main themes are approximation theorems (including bounding the loss in revenue or welfare due to lack of information, to strategic behavior, or to impracticality of the optimal mechanism); various notions of complexity (including computational complexity, communication complexity, and sample complexity); and cryptographic tools (including cryptographic commitments, multiparty computation, and zero-knowledge proofs). Economic applications mostly include analysis of equilibria, pricing, and mechanism design.

Computing at Scale

COMPSCI 2420 2024 Fall

Specialized AI accelerators enable efficient AI computations for a variety of tasks at various scales using a wide range of parallel, distributed, and embedded computing platforms. For example, in generative AI applications such as ChatGPT and Stable Diffusion, these accelerators allow for (1) distributed model training and low-latency, high-throughput inference serving in the cloud, and (2) efficient private training and inference using local knowledge on resource-constrained edge devices. In this course, students will learn systematic methods for implementing parallel computations for computer vision and language models on numerous computing cores or nodes. They will also learn techniques for co-designing machine learning models, data curation methods, computing algorithms, and system architectures. Upon successful completion of this course, students will be equipped to tackle the challenging tasks of designing and utilizing energy-efficient, high-performance AI accelerators.

Advanced Computer Networks

COMPSCI 2430 2024 Fall

This is a graduate-level course on computer networks. This course offers an in-depth exploration of a subset of advanced topics in networked systems. We will discuss the latest developments in the entire networking stack, the interactions between networks and high-level applications, and their connections with other system components such as compute and storage. In this year's edition, we will use machine learning as a prime example to understand its unique requirements and challenges in the context of networking. As machine learning applications increasingly rely on larger models and faster accelerators, the demand for enhanced networking capabilities becomes imperative. Throughout this course, we will study cutting edge networking solutions and principles for  co-designing networks with compute and storage, to meet the evolving needs of machine learning applications. The course will include lectures, in-class presentations, paper discussions, and a research project.

More information of this course is at  https://github.com/minlanyu/cs243-site .

Advanced Topics in Programming Languages

COMPSCI 2520R 2024 Fall

Seminar course exploring recent research in programming languages. Topics vary from year to year. Students typically read and present research papers, undertake a research project.

Formal Methods for Computer Security

COMPSCI 2540 2025 Spring

Stephen Chong Tuesday, Thursday 11:15am to 12:30pm

This course explores formal methods for computer security, including formal security models, relationships between security properties/policies and enforcement mechanisms, principled techniques and tools to specify, analyze, and construct secure computer systems. Specific topics include properties, hyperproperties, side channels, reasoning about cryptographic protocols, information flow, authorization logics, and verification techniques. Assessment will include homeworks and/or small projects during the semester as well as a final, larger project that is open-ended and driven by student interests.

Research Topics in Operating Systems

COMPSCI 2610 2025 Spring

Eddie Kohler Monday, Wednesday 5:15pm to 6:30pm

An introduction to operating systems research. Paper-based seminar course that introduces students to the state of the art in systems research through historical and quantitative lenses. Students will read and discuss research papers and complete a final research project.

Introduction to Distributed Computing

COMPSCI 2620 2025 Spring

Jim Waldo Monday, Wednesday 2:15pm to 3:30pm

An examination of the special problems associated with distributed computing such as partial failure, lack of global knowledge, asynchrony and coordination of time, and protocols that function in the face of these problems. Emphasis on both the theory that grounds thinking about these systems and in the ways to design and build such systems.

Systems Security

COMPSCI 2630 2024 Fall

This course explores practical attacks on modern computer systems, explaining how those attacks can be mitigated using careful system design and the judicious application of cryptography. The course discusses topics like buffer overflows, web security, information flow control, and anonymous communication mechanisms such as Tor. The course includes several small projects which give students hands-on experience with various offensive and defensive techniques; the final, larger project is open-ended and driven by student interests.

Big Data Systems

COMPSCI 2650 2025 Spring

Big data is everywhere. A fundamental goal across numerous modern businesses and sciences is to be able to utilize as many machines as possible, to consume as much information as possible and as fast as possible. The big challenge is how to turn data into useful knowledge. This is a moving target as both the underlying hardware and our ability to collect data evolve. In this class, we discuss how to design data systems, data structures, and algorithms for key data-driven areas, including relational systems, distributed systems, graph systems, noSQL, newSQL, machine learning, and neural networks. We see how they all rely on the same set of very basic concepts and we learn how to synthesize efficient solutions for any problem across these areas using those basic concepts.

Conceptualizing, Building, and Evaluating Usable Novel Interactive Systems

COMPSCI 2780 2025 Spring

Students learn critical techniques, concepts, and technologies for building usable novel interactive systems, alone and in teams. Assignments provide hands-on experiences with different modern frameworks, platforms, and libraries while conceptual commonalities and distinctions are annotated and explained at multiple levels, from the programming environments to the interfaces users interact with. Discussions grounded in readings will also cover human cognitive capabilities, iterative prototyping, and human-AI interaction. The final research project requires iteratively designing and building a novel interactive system informed by pilot user studies and a final evaluation with target users. Designed for PhD students interested in HCI or using interactive systems as tools for discovery in other fields.

Research Topics in Human-Computer Interaction

COMPSCI 2790R 2024 Fall

Elena Glassman, Katy Gero Monday, Wednesday 1:30pm to 2:45pm

Students will read, write about, prepare presentations about, and discuss human-computer interaction (HCI) and HCI-relevant work with a focus on papers about interfaces and automation that work especially well with (or clash against) human cognitive capabilities. Papers will primarily be on the building and evaluation of novel systems, as well as theories of and studies characterizing human cognition relevant to human-AI interaction scenarios. As a semester-long final project, students will pursue a research project of their own design in self-organized groups and present their findings in writing and orally in a conference-style format, as means to understand more deeply the processes behind HCI research.

Topics in Machine Learning: Computational Properties in Interpretable Machine Learning

COMPSCI 2822R 2024 Fall

Finale Doshi-Velez Monday, Wednesday 9:45am to 11:00am

There has been growing interest in recent years for machine learning systems that are somehow transparent about their inner workings -- whether it be that the entire system is inherently interpretable, or that a single decision can somehow be explained. However, the question of what approach is best for what context remains elusive. In this course, we will focus on computational properties of interpretable machine learning methods, such as faithfulness or stability. Assessing methods with respect to these properties may allow us to rule out poorly-performing approaches without the need for expensive user studies. By categorizing methods by their computational properties, we will also be able to start thinking about which methods might be useful for a specific context. After a few initial assignments, the course will be focused on reading papers, discussion, and a semester-long project.

Advanced Computer Vision

COMPSCI 2831 2024 Fall

Todd Zickler Tuesday, Thursday 12:45pm to 2:00pm

Vision as an ill-posed inverse problem: image formation, two-dimensional signal processing; feature analysis; image segmentation; color, texture, and shading; multiple-view geometry; object and scene recognition; and applications.

AI for Social Impact

COMPSCI 2880 2024 Fall

Milind Tambe Monday, Wednesday 3:45pm to 5:00pm

Recent years have seen AI successfully applied to societal challenge problems. Indeed, recognizing the potential of AI for tremendous social impact in the future, "AI for social impact" is growing as a subdiscipline within AI. In this course, we will discuss successful case studies of  use of AI for public health, environmental sustainability, public safety and public welfare. Simultaneously, we will discuss key foundations of the area of AI for social impact. To that end, among other topics, we will focus on challenges in AI for Social Impact, what makes projects successful, how to investigate project impact in the field and ethical considerations for such projects. A key part of this course will be AI4SI projects with non-profits.

Seminar on Effective Research Practices and Academic Culture

COMPSCI 2901 2024 Fall

John Girash, Madhu Sudan Friday 9:45am to 11:45am

This is a reading and discussion-based seminar designed for entering Computer Science Ph.D. students. This course prepares students to manage the difficult and often undiscussed challenges of Ph.D. programs through sessions on research skill building (e.g. paper reading, communication), soft skill building (e.g. managing advising relationships, supporting your peers), and academic culture (e.g. mental health in academia, power dynamics in scientific communities), as well as research and professional-oriented discussions. This is a full-year, 4-unit course, meeting once a week in each of the fall and the spring. Students must complete both terms of this course (CS 2901 and CS 2902) within the same academic year to receive credit.

COMPSCI 2902 2025 Spring

Special Topics in Computer Science

COMPSCI 2990R 2024 Fall

Madhu Sudan

Supervision of experimental or theoretical research on acceptable problems in computer science and supervision of reading on topics not covered by regular courses of instruction.

COMPSCI 2990R 2025 Spring

In Computer Science

• First-Year Exploration
• Concentration Information
• Secondary Field
• Senior Thesis
• AB/SM Information
• Student Organizations
• How to Apply
• PhD Timeline
• PhD Course Requirements
• Qualifying Exam
• Committee Meetings (Review Days)
• Committee on Higher Degrees
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Lecture Series
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories

900 Free Computer Science Courses from the World’s Top Universities: The Ultimate Guide for Learners and Developers

As a full-stack developer and lifelong learner, I‘m fascinated by the world-class computer science education now available online for free. Motivated learners anywhere can access courses from the same prestigious universities that tech leaders like Mark Zuckerberg and Larry Page attended.

In this ultimate guide, I‘ve compiled 900 online courses offered by the 60 best universities globally for studying computer science. Using a data-driven ranking system, these elite institutions represent the top 1% in computer science education.

Whether you‘re looking to launch a new career, enhance your skillset, or simply explore a passion for technology—these courses allow you to learn from the absolute best. Read on to discover this exclusive selection of programs and unlock transformative opportunities.

Crafting the List: Methodology

To create this guide, I took a calculated, objective approach. I began by identifying the most authoritative global CS program rankings:

• QS World University Rankings
• Times Higher Education
• Shanghai Rankings

Next, I aggregated the rankings by averaging each university‘s score across all three lists. This produced a consolidated top-60 representing the leading institutions.

I then filtered out any without substantial free online courses and supplemented with the comprehensive Class Central course catalog. The final output is a structured list of 900 high-caliber courses from only top-tier providers.

Figure 1: Ranking computer science universities and selecting top courses

You can verify the raw ranking data and full methodology on GitHub if interested in more details.

Overall this ensures every course comes from a prestigious program ranked globally in the top 1% for computer science.

Why Learn from These Universities?

Besides the obvious reputation and expertise these elite institutions carry, what tangible value do they offer? Here are key advantages of learning from top-ranked universities:

Cutting-Edge Education

Top programs continually update their curriculum with the latest advancements. Their courses reflect real-world practices better than most online resources.

Industry Connections

They frequently consult and collaborate with tech leaders to align their courses with employer needs. Their grads seamlessly enter top companies.

Specialization

With extensive capabilities, they offer more specialty and emerging fields than generic courses. From quantum computing to biometrics, their diversity stands out.

Credential Value

While mostly providing free courseware without credits, some offer full degrees, MicroMasters, and respected certificates.

Networking Access

Enrolling grants access to engaged student and alumni communities. This delivers networking and mentors rarely found in self-paced courses.

In summary, elite schools get elite results. Let‘s examine the highest ranking in detail:

Top 10 Universities for Computer Science

The leading programs globally for studying computer science are:

• Stanford University
• Carnegie Mellon
• University of Cambridge
• University of California—Berkeley
• University of Oxford
• Harvard University
• Tsinghua University
• National University of Singapore (NUS)
• Princeton University

Review the full consolidated ranking on GitHub .

Now let‘s showcase some of their highly popular courses among the 900 included:

Notable Computer Science Courses

While every course listed later delivers value, these select few stand out as all-time classics:

• Programming for Everybody (Getting Started with Python) from University of Michigan (40023 reviews) ⭐
• Machine Learning from University of Washington (40 reviews) ⭐
• CS50‘s Introduction to Computer Science from Harvard University (159 reviews) ⭐

See Class Central‘s top computer science courses here for more fantastic options.

Next, I‘ll break down offerings into subjects so you can access exactly what you need.

Subject-Based Computer Science Courses

I‘ve categorized courses across core computer science domains:

Artificial Intelligence

From neural networks to computer vision, 34 courses help you build intelligent systems:

• Machine Learning : Master predictive analytics to teach computers new capabilities (68 courses) ⭐
• Deep Learning : Implement complex neural networks like those in AI assistants (8 courses)
• Computer Vision : Enable computers to identify and classify images and videos automatically (18 courses)
• Self-Driving Cars : Develop integrated software stacks to enable autonomous navigation (7 courses)

Artificial intelligence represents an especially hot field with strong employer demand today. These courses deliver rare access to expertise from elite AI research groups.

Core Computer Science

Across 86 courses, gain broad computer science knowledge that underpins all sub-domains and roles:

• Algorithms and Data Structures : Implement processing methods and information storage formats to manage complexity and optimize performance (88 courses) ⭐
• Databases : Store, organize, and query datasets using systems like SQL and NoSQL databases (24 courses)
• Bioinformatics : Apply computational techniques to biological data for discoveries that advance medicine (25 courses)
• Blockchain : Build tamper-proof distributed ledgers to verify transactions and agreements (without centralized control) through blockchain platforms like Bitcoin and Ethereum (6 courses)
• Cryptography : Protect information and privacy with encryption like that enabling secure connections to websites (6 courses)
• Quantum Computing : Leverage quantum mechanical phenomena for new paradigms in computing (17 courses)

I especially recommend the algorithms and data structures courses. Mastering these foundations unlocks the potential to then learn almost any other domain.

Data Science

Make sense of complex data to reveal valuable insights across 74 courses:

• Data Analysis : Manipulate, process, visualize, and statistically evaluate datasets using languages like Python and R (74 courses) ⭐
• Big Data : Store and work with massively large and complex datasets using systems like Hadoop and Spark (21 courses)
• Data Visualization : Present findings visually through engaging charts, graphs, and interactive reports (13 courses)
• Data Mining : Apply machine learning models to large information repositories to segment, classify, and predict key elements (8 courses)

Data analytics now drives decision making across industries. These courses teach you the tools to handle the world‘s exponentially increasing data volumes.

Software Programming & Development

Across 75 courses, build applications and systems leveraging languages like Java, Python, C++ and platforms ranging from mobile to cloud computing:

• Programming Languages : Master languages like Python (38 courses), SQL (12 courses), Scala (8 courses) and Java (11 courses)
• Web Development : Create dynamic browser-based applications using JavaScript, HTML/CSS and frameworks like React (38 courses) ⭐
• Mobile Development : Build Android and iOS apps even without prior coding experience (24 courses)
• Software Development : Modularly construct reliable code and integrate systems through Software Development Lifecycle methods like agile (19 courses)

Programming represents the fundamental gateway into launching or elevating a technology career today. These courses provide expert guidance unteachable through DIY tutorials.

Information Technology (IT)

Understand critical technologies supporting modern systems, networking and infrastructure across 13 key courses:

• Internet of Things (IoT) : Construct devices with connectivity and data exchange capabilities (23 courses)
• Cloud Computing : Migrate systems to infinitely scalable on-demand servers accessible online (10 courses)
• Healthcare IT : Apply systems to augment patient care and medical discoveries through electronic records, data science techniques and virtualization (23 courses)

IT infrastructure now forms the backbone of most major organizations. These skills become increasingly crucial even in non-technical roles.

This guide should spark exciting knowledge journeys through elite computer science courseware. Let‘s recap key takeaways:

Key Highlights and Recommendations

Learn from the best. The 60 universities here represent the top 1% globally for computer science education.

Save money. All courses are freely available, though some offer paid credentials.

Gain expertise. Programs come directly from world-renowned computer science departments.

Pick selectively. With 900 courses, browse by subject to find your specific interests.

Combine courses. For example, learn Python programming alongside Data Analysis to maximize employable abilities.

Apply learnings. Capstone projects, coding exercises and discussions keep concepts practical.

Now discover the full selection of elite courses available:

The 900 Best Free University Computer Science Courses

Courses particularly recommended are marked with a star (⭐). Additional subject areas not expanded earlier also appear now.

Artificial Intelligence Courses (32)

Machine learning courses (68), deep learning courses (8), computer vision courses (18), self-driving cars courses (7), computer science courses (86), algorithms and data structure courses (88), databases courses (24), bioinformatics courses (25), blockchain and cryptocurrency courses (6), human-computer interaction courses (9), computer networking courses (7), cryptography courses (6), quantum computing (17), data science courses (74), data analysis courses (74), big data courses (21), data visualization courses (13), data mining courses (8), programming courses (56), python courses (38), sql courses (12), scala courses (8), java courses (11), c++ courses (8), software development courses (19), web development courses (38), mobile development courses (24), game development & vr courses (8), information technology courses (13), internet of things courses (23), healthcare informatics courses (23), cloud computing & devops courses (10), network security courses (3), entrepreneurship courses (4).

The intersection of technology and business creates massive opportunities today. Gain entrepreneurial guidance tailored to launching tech ventures through these courses:

• Startup Engineering from Stanford University (8 reviews) ⭐
• Digital Health Entrepreneurship from Johns Hopkins University
• Cyber Physical Systems Entrepreneurship from University of California, San Diego
• Intellectual Property Law and Policy from University of Pennsylvania

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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Computer Science Degree Guide: Courses, Careers And Online Options

Published: Jun 12, 2024, 2:55pm

Key Takeaways

• A computer science degree prioritizes software design, development and maintenance.
• Computer science majors can specialize in subfields such as artificial intelligence, data analysis and information security .
• ABET’s Computing Accreditation Commission is the accrediting body for computer science bachelor’s degree programs.

Historically, students at various levels had to attend traditional classroom lectures to graduate and earn their degrees. But with the steady advancement in digital learning technologies, distance learning has come to stay.

Computer scientists made that happen.

Your smartphone, messaging apps and the AI software you use daily are all testaments to the impact IT professionals have made worldwide.

If you want the opportunity to create a digital solution that can improve people’s quality of life, a computer science bachelor’s degree is the right place to start. This program will prepare you for software development, web design and cybersecurity careers.

Why You Can Trust Forbes Advisor Education

Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.

• 6,290 accredited, nonprofit colleges and universities analyzed nationwide
• 52 reputable tech bootcamp providers evaluated for our rankings
• All content is fact-checked and updated on an annual basis
• Rankings undergo five rounds of fact-checking
• Only 7.12% of all colleges, universities and bootcamp providers we consider are awarded

What Is a Computer Science Bachelor’s Degree?

Computer science is generally understood as the study of computers and computational systems. These computational systems are, in turn, applied to solve real-life problems across diverse sectors. Unlike computer engineers who typically work with computer hardware, computer science majors are more concerned with software, its development and applications.

Degree Finder

Typical curriculum.

A computer science bachelor’s degree is an undergraduate program that covers all the theoretical and practical aspects of designing, developing and testing software.

Coursework for this major may differ from school to school, but subject areas generally include ethics in technology, database systems, data structures, operating systems, artificial intelligence and programming.

After Graduation

Computer science graduates can apply their skills in various industries including IT, sports, healthcare, finance and construction. During the bachelor’s program, students are encouraged to specialize in a subfield of computer science that aligns with their career interests.

Some concentrations offered in computer science undergraduate programs are artificial intelligence and machine learning, computing systems, information security, data analysis and software engineering.

Program Length

It takes about four years of full-time study to earn a bachelor’s in computer science. Part-time students, however, may need more time to fulfill the 120-credit requirement for this degree. Computer science bachelor’s programs generally culminate in a capstone project, but some schools may accept an academic internship in lieu of the capstone project.

Accreditation for Computer Science Degrees

ABET provides accreditation to computer science bachelor’s degree programs. Graduating from an accredited computer science program proves to prospective employers and industry peers that you have received sufficient training to join the global workforce of IT specialists.

Admission Requirements for a Bachelor’s in Computer Science

To get admitted into most computer science undergraduate programs, you need:

• A high school diploma or GED® equivalent
• Academic transcripts that demonstrate a history of advanced mathematics training
• An acceptable high school GPA (aim for a 3.0 GPA or higher on a four-point scale )

Specializations for a Computer Science Degree

Computer science majors can concentrate in one of the following specializations.

Artificial Intelligence and Machine Learning (AI & ML)

Artificial intelligence is the ability of computers to simulate human intelligence, while machine learning refers to the algorithms that make the creation of AI systems possible.

The AI & ML concentration in a computer science major teaches students to develop AI & ML solutions for real-world problems, analyze large datasets with advanced computing tools and use AI & ML tools ethically.

In addition to the career options open to computer science graduates, those specializing in artificial intelligence and machine learning can also work as machine learning engineers, business intelligence developers, data mining analysts, natural language processing analysts and data scientists .

Information Security

The information security specialization covers cybersecurity procedures and the fundamentals of computer networking. Students in this track learn how to protect networks from cyberattacks through courses such as computer networking, cybersecurity foundations, systems security, operating systems and ethical hacking.

This concentration is ideal for students interested in becoming network security analysts, security systems administrators, database administrators and IT support specialists.

Data Analysis

The data analysis specialization in computer science introduces students to the day-to-day responsibilities of data analysts, which include overseeing data systems, creating database environments and interpreting statistical information using computing tools.

Courses in the data analysis track may include big data, data validation, structured database environments and regression analysis.

Common Courses in a Computer Science Degree Program

Courses vary from program to program, but most computer science bachelor’s degrees require courses similar to the following.

Introduction to IT

This course introduces students to the fundamentals of information technology and the various disciplines within the IT field, including networking, systems security, data management, scripting and programming.

It also covers the history of information systems and how the subsets of IT relate to each other.

Operating Systems for Programmers

An operating system is a program that manages all other applications and resources on a computer through functions such as memory management, CPU allocation and task scheduling.

In this course, learners are exposed to how computers process data, the function of operating systems in application development and the techniques of process scheduling.

Software Design and Quality Assurance

This course emphasizes the need for quality assurance throughout the software development cycle. Quality assurance in software testing is a procedure necessary to ensure the quality of software products delivered to the end user. It involves code reviews, user testing, automated testing and test-driven development.

To help learners understand the important quality assurance techniques, this course covers topics such as best practices for software analysis, testing strategies and quality planning.

Back-End Programming

Back-end developers build all the infrastructure that powers functional applications and software. Their work is behind the scenes, ensuring that servers and databases run efficiently. The back-end programming course equips students with the skills to create the back-end components of a web application, using programming languages such as Java, Python, PHP, C#, Ruby and SQL.

What Can You Do With a Computer Science Bachelor’s Degree?

We sourced salary data for this section from the U.S. Bureau of Labor Statistics in June 2024.

Computer Programmer

Median Annual Salary: $99,700 Minimum Required Education: Bachelor’s degree in computer science Job Overview: Computer programmers write, test and modify the code that brings software to life. They write programs in various programming languages such as Java, C++, SQL and Python.

Database Administrator

Median Annual Salary: $117,450 Minimum Required Education: Bachelor’s degree in computer science or a related field Job Overview: Database administrators use specialized software to design, maintain and restore databases to ensure the safety of an organization’s data.

Information Security Analyst

Median Annual Salary: $120,360 Minimum Required Education: Bachelor’s degree in computer science or a related field Job Overview: Information security analysts detect flaws in a company’s security architecture. They also research the latest cybersecurity trends, install firewalls and set up data encryption software.

Software Quality Assurance Analyst

Median Annual Salary: $101,800 Minimum Required Education: Bachelor’s degree in computer and information technology ; master’s degree sometimes preferred Job Overview: Software quality assurance analysts are responsible for ensuring the reliability and quality of applications throughout the development life cycle. They use specific testing strategies to assess software performance, report any defects in the program and recommend solutions to enhance user experience.

Web Developer

Median Annual Salary: $84,960 Minimum Required Education: High school diploma; bachelor’s degree in an IT field, sometimes required Job Overview: Web developers design and maintain websites, in accordance with clients’ needs. While back-end web developers are concerned with the site’s framework and functionality, front-end developers prioritize the visual and interactive elements of the website.

Should You Earn a Computer Science Degree Online?

Not all degrees can be earned online. Disciplines with a practicum component , such as medical science, typically require on-campus presence. However, some other majors are more flexible, because their practical requirements can be completed anywhere.

Before deciding to earn your online computer science degree, consider the following factors:

• Is the program accredited? Ensure that your computer science program is accredited by ABET’s Computing Accreditation Commission. Programmatic accreditation is important for several reasons. It lends credibility to your program and indicates that you’ve been adequately trained for any role in your field.
• How flexible is the program? Earning your degree online might be beneficial if you have a job and other obligations. Moreover, students on campus and distance learners generally follow the same curriculum, so your location has little to do with the quality of education you’ll receive.
• Is it budget-friendly? Getting a degree is quite expensive, especially if you’re an out-of-state student. Online degrees, however, are cost-effective, as students can save on transportation and housing instead of commuting to and fro.

Featured Online Schools

Learn about start dates, transferring credits, availability of financial credit and much more by clicking 'Visit Site'

Frequently Asked Questions (FAQs) About Computer Science Degrees

Is computer science a good degree.

Computer science is a great major because it can unlock numerous career paths across diverse sectors. Computer science graduates can work as data analysts, software engineers, database administrators, web designers and so much more.

What is a degree in computer science?

A computer science bachelor’s degree is a qualification that proves completion of a four-year program in computer science. It demonstrates that the degree holder has acquired the skills required to compete in the global IT workforce.

How hard is a computer science degree?

Studying computer science in college may be difficult for people who have no basic IT knowledge. With constant practice, however, it gets easier to understand and apply computing concepts.

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Roosevelt University

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Master’s in Computer Science, MS

Technology changes fast. Stay competitive with a master’s in computer science. At Roosevelt, you can upgrade your knowledge or make a career change into a fast-growing, sought-after field.

Why Roosevelt for Your Master’s

Individualized learning

In small, intimate classes, you’ll make personal connections with your classmates and professors. You’ll work closely with your instructors to meet your academic and career goals.

Career Changers and Aspiring Leaders Welcome

Your faculty members have industrial or research experience that can help you train for your career. Roosevelt students assume leadership positions in start-ups, national companies, large data warehouses and cloud computing.

New to Computer Science? Our three-course bridge program will prepare you to excel in the master’s degree.

Join a growing field

Computer and Information Research jobs are projected to grow 23% from 2022 to 2032, much faster than average, according to the U.S. Bureau of Labor Statistics.

Interested in cyber security? Explore our master’s program.

Admission Requirements

Sample courses, career opportunities, admission info.

Location: Chicago Start Term: Fall, Spring

• Applications can be completed entirely online.
• $40 application fee.
• Official or unofficial transcripts from all previously attended colleges and universities in the United States.

To contact an admissions counselor click here . 

Sample Computer Science Courses

• Data Mining
• System Programming
• Game Theory and Application
• Intelligence Systems
• Machine Learning

View the course catalog for the MS in computer science.

Computer Science Careers

Today, every company is a tech company. Roosevelt alumni thrive in software engineering and AI-applications after graduation. Alumni have also gone on to doctoral programs.

Our graduates hold titles like:

• Software engineer
• Software developer
• Data scientist
• Computer system analyst
• Database administrator
• Systems Architect

Roosevelt alumni have secured jobs with:

• Blue Cross and Blue Shield
• Walmart Global Tech
• Beacon Funding

Explore our master’s program in cyber security and information assurance.

"The faculty, staff and the programs that I’m involved in have prepared me for success post-graduation."  

Liz Moreno, BS ’20, MS ’21

Meet your Computer Science Faculty

Explore More Programs

From biology to bassoon, psychology to pharmacy, reading to real estate, Roosevelt has a program for you. Explore our comprehensive academic choices, outstanding faculty and nearly limitless degree program options.

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Computer Science Major

Technology honor society, contact admission.

• 617-333-2210
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B.S. in Computer Science

Computer Scientists design and create the computer systems that organize and simplify our life every day. They begin with the laws of physics and end up with today’s (and tomorrow’s) amazing devices that make our world smaller and more interesting.

Computer Science majors enter careers such as software engineering, applications engineering, systems engineering, network design and administration, software controlled networking, data science, web development, user experience design, systems architect, game development, mobile development, cloud architecture and development, computer hardware development, computer and network security, DevOps, and test engineering.

• Computer Science Major Requirements and Learning Outcomes

Learn Practical Methods and Skills, Attractive to Employers

• Clean Coding Practices
• Cloud Development
• Cybersecurity
• Data Sciences
• Software Engineering
• User Experience Design

Courses You’ll Love

Data Structures and Algorithms Modern Application Development Environments Programming and Problem Solving Programming Languages Software Engineering User Experience Design Web Development Project

You may also like...

• Business Administration Minor
• Criminal Justice Minor
• Mathematics Minor
• Video Gaming Studies Minor

Careers in Computer Science

Our alumni in the field.

The U.S. Bureau of Labor Statistics projects the computer and information technology field to grow by roughly 13 percent by 2026 and recent data shows computer science job openings far exceeding U.S. degree production. Careers for Computer Science majors include tech roles across several industries that include both the public and private sector, healthcare, education, financial services, ecommerce and brick-and-mortar companies, that could include the following employers:

• Federal Reserve Bank
• Mass General Hospital
• National Security Administration
• Sony Entertainment Network
• State of Massachusetts

Dream Beyond the Classroom

Get real-work project experience – Computer Science majors participate in a two-semester senior project course that will allow students to solve a real business problem for a real-world client.

Join our on-campus community – Student chapters include ACM (Association for Computing Machinery) and ACM-W (Association for Computing Machinery-Women) and IEEE (Institute of Electrical and Electronics Engineers).

Graduate with distinction – Join the Curry College Delta Chi Chapter of the Epsilon Pi Tau Honor Society, an international honor society dedicated to the technology fields.

Pursue an advanced degree – Computer Science majors frequently go on to graduate school studying topics as diverse as theoretical physics and business. Curry students have pursued graduate degrees at a variety of schools including Boston University, Syracuse University, and Northeastern University.

Attend industry events – Network with the pros at professional conferences such as Boston Code Camp and more.

Present among the best – Computer Science students at Curry are often invited to present a professional session at leading industry events and area Code Camps.

Laptop Requirements for Majors

The minimum laptop requirements for Accounting, Business Administration, Marketing, Sport and Recreation Management, and Computer Science majors are:

• Windows 10 or 11 operating system
• Minimum 64-bit Intel I-7 or 64-bit AMD Ryzen 7
• Minimum quad core processor
• Minimum 8 GB memory
• Minimum 500 GB internal storage

NOTE: MacBooks and Chromebooks do not qualify.

Explore Related Programs:

Curry College is a member of the Delta Chi Chapter of Epsilon Pi Tau. Epsilon Pi Tau is an international honor society dedicated to the technology fields and recognizes the academic excellence of students studying technology related fields at Curry, including the Computer Science major.

Study Abroad

Go global with Curry College faculty members as part of our very popular Short-term, Faculty-led Courses, or create your own customized Study Abroad opportunity!

First-Year Experience

Making the transition to college can be a little confusing and lot of fun. Your First-Year Experience at Curry College helps smooth out the bumps and puts you on the path to success.

At the heart of Curry College's undergraduate curriculum is our General Education (Gen Ed) Program. Gen Ed is based on our belief in the power and potential of the liberal arts.

Life-Changing Opportunities Await

Start with a foundation in the liberal arts. Add attentive faculty and countless opportunities to learn by doing. That’s what you’ll get with a bachelor’s degree from Curry. Learn what’s waiting for you today.

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Computer Science Education

About the program.

The computer science education program is designed to teach students principles of computing as well as pedagogical theories to facilitate learning in the fields of computer science and web programming. Students will learn how to demonstrate computer science and web programming concepts in a secondary education environment and be able to evaluate a student’s computer programming performance.

B.S. in Computer Science Education  

120 Credits to Graduate

The computer science education degree provides a solid foundation of software and web development skills specifically for secondary educators. It consists of computer science and web development classes as well as education courses necessary to obtain a Utah teaching license with endorsements in computer science and web development.

Helpful Links

• Code of Ethics
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• Advising Sheet (Fall 2023)
• Degree Flow Chart (2023-24)

Need Some Homework Help?

General tutoring is available to all UVU students and UVU employees. Walk-in and by-appointment tutoring is available during the lab hours. Appointments are made between the IA and the students registered for the course the IA assists with. Walk in to CS 726 from 11-6 M-F or 11-2 on Saturdays to meet with an instructional assistant!

Testimonials

"This program...ensures that graduates are well-prepared for the demands of the industry."

"The computer science program at Utah Valley University provides an exceptional learning experience. As a faculty member, I have witnessed firsthand the dedication of our students and the commitment of our department to providing top-notch education and resources. The program's focus on both theoretical concepts and practical application ensures that graduates are well-prepared for the demands of the industry."

Dr. Lynn Thackeray, Assistant Professor

Utah Valley University

• Diversity in IT

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Number of women choosing computer science degrees up 8%

Computer science has a persistent gender divide, but research by bcs has found more women are now being accepted on computing university courses.

• Clare McDonald, Business Editor

There has been an 8% year-on-year increase in the number of women studying for computer science degrees in the UK, according to research by BCS, The Chartered Institute for IT.

Looking at data from university admissions service UCAS, BCS found that of the 15,530 UK-based students accepted to study computer science this year, 2,940 were women.

Julia Adamson, managing director for education and public benefit at BCS, said: “It’s fantastic that the overall number of people taking computer science is increasing, but there remains a huge demand for more skilled people to meet the needs of our digital future.

“The growth in the number and the diversity of qualified technologists needs to continue to accelerate, not only to help close the gender gap, but also to meet future needs of the UK economy.”

The number of women in the technology sector has been increasing painstakingly slowly over the past five years, and with women now making up around 20% of technologists in the UK , there’s still more work to do.

There are a number of reasons girls are less likely to choose a computing career than boys, including a lack of visible and accessible role models to inspire them to pursue tech careers, misconceptions about tech roles and those who work in them, and a lack of inclusive culture in the sector, leading women to choose an alternative career – sometimes even if they’ve already chosen tech .

Read more about diversity in tech

• At a Computer Weekly diversity in tech event, in partnership with Nash Squared, more than 100 experts from the tech and employment sectors shared their ideas for improving diversity in the technology industry .
• Half of IT workers in Europe believe there is good representation of women in leadership positions in tech, despite numbers telling a different story .

Meanwhile, research by McKinsey found 31% of girls across Europe studying tech-based subjects at school don’t go on to become tech undergraduates , despite around a third of girls who drop science, technology, engineering and maths (STEM) subjects subsequently regretting this decision once they realise how widespread the need for technology skills is, even outside of the industry itself.

Thankfully, there has been some progress along the pipeline, with the number of girls choosing computing at GCSE level increasing for the second consecutive year in 2023, increasing by 28.6% at A-level this year compared with 2023, and an 8% increase in women choosing computer science degrees in 2024.  

The ratio of male to female students applying for and being accepted to study computer science degrees at university has been improving gradually over the past five years.

In 2019, when BCS first started monitoring the number of women accepted to study computer science at degree level in the UK, for every woman enrolled on a computer science degree there were 5.3 men. This year, there are 4.1 men for every woman taking computer science at degree level.

Last year, BCS noticed one of the largest jumps in women and students overall opting to study computing-based subjects at university – 2023 showed an 18% year-on-year increase in the number of women applying to study computing at university – most likely because of the growing awareness of technologies such as artificial intelligence (AI) and cyber security .

But university is not the only answer for a successful technology career, with options such as apprenticeships contributing towards closing the skills gap by internally training individuals for the job they will go on to do, ensuring they have the appropriate skillset.

Read more on Diversity in IT

BCS report shows lack of improvement in tech diversity

Computer Weekly announces the Most Influential Women in UK Tech 2023

Vote: Who should be the 2023 Most Influential Woman in UK Tech?

Computing degrees more popular due to AI, says BCS

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Access to Higher Education Diploma (Computer Science) – Level 3

Course overview

This course is for you if you are interested in pursuing a career in the computing and digital industry. This course will give you the skills needed to move on to University.

Modules offered may vary. Modules will include: Computer Systems Cyber Security Database Applications Introduction to Computer Architecture Introduction to programming Systems Analysis and Design Maths for Computing Web Page Design & Production

September – June Students will study 2 days per week, between 9am – 1pm with online study.

You must complete a successful interview and be working at a Level 2 English and maths. Applicants must be aged 19+.

You will be assessed on a continuous basis, both practically and theoretically throughout the course. Assessment methods will be flexible and creative and will echo those used in higher education.

Careers/University Opportunities Career opportunities include web design, web development, IT consulting, IT systems management, systems analysis, games development, data analysis and programming. Past students have progressed to study degrees in computing, computer science, software development, web development, app design, artificial intelligence, networked systems, digital games, games design and production.

£3,928 Tuition fees are free if you are a UK or home- fee status student and aged 16-18 on the 31st August before the start of your course. If you are aged 19 or over, you may be required to pay fees depending on your residency status, educational background and personal circumstances. Advanced Learner Loan: If you are aged 19-23 and this is not your first full Level 3 qualification or you are aged 24 or over on the start date of the course, you will have to pay fees. However, you may qualify for an Advanced Learner Loan. You can apply for an Advanced Learning Loan from Student Finance England (SFE) to fund all or part of the fee. For information on Advanced Learning Loans go to www.gov.uk/advanced-learning-loans/ . Please note any fee shown excludes any additional costs to learners for any necessary equipment, materials, uniforms or any visit/residential that learners are required to participate in.

You can apply online by clicking on the apply button at the top of this website page. Once we have received and processed your Application Form, we will be in touch. Click here to download the application form. If you have any queries with regards to your application or you would like further information and support, please contact our Admissions Team on 01642 333601, or if you have any enquiries about courses, please contact Course Information on 01642 333333 or e-mail [email protected]

Explore Access to HE

Access diplomas are the perfect choice for mature learners who have been out of education for a while. Our courses offer flexible and family friendly hours to fit around busy lifestyles.

Not sure what career path is right for you? Take the career path assessment.  

Visit Middlesbrough College

Come and visit us on one of our college open days, where you can find out more about our courses, check out our facilities and speak to current students and staff.

Additional Information

We understand students may require a little extra help during their studies, which is why our learning support team are on hand to help learners with a learning difficulty or support need to achieve their full potential.

Whatever your age or level of study, we offer a comprehensive support service and bursary scheme to help you through your studies. From childcare assistance to travel packages, we pride ourselves on giving expert advice and support.

We are situated in a fantastic location, just a stone's throw from Middlesbrough town centre. As well as being a matter of minutes from both the train and bus stations, eligible students can also benefit from free travel.

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How to Choose the Right AI Course for Your Career Goals

• 21 Aug 2024

Artificial Intelligence (AI) is no longer reserved for tech giants and computer scientists; it’s an innovation catalyst across industries. According to a report by management consulting firm McKinsey , 72 percent of organizations have adopted AI, with half now using it in two or more business functions—up from less than a third the year prior.

Whether you want to integrate this technology into your current role or lead your organization through a digital transformation, choosing an AI course that aligns with your career goals is crucial.

But do you need a computer science background to excel in AI courses?

Access your free e-book today.

Do AI Courses Require a Background in Computer Science?

There’s a common misconception that AI is a highly technical tool. The reality is that any professional can leverage it to enhance efficiency and drive business innovation .

“I have a strong belief that the future of business is going to be AI-powered,” says Harvard Business School Professor Karim Lakhani , who co-teaches the online course AI Essentials for Business with HBS Professor Marco Iansiti. “There’s not one organization, one role that will not be touched by AI tools.”

AI courses have become increasingly accessible, even for those without a computer science background. HBS Online’s AI Essentials for Business course introduces foundational concepts to ensure learners from diverse backgrounds understand different applications of AI, such as machine learning and predictive modeling, without being overwhelmed by technical jargon.

This inclusivity ensures that professionals from all industries can gain the skills to harness AI's potential. Your success depends more on your problem-solving skills, creativity , and dedication to learning than on prior technical knowledge.

As you consider which AI course best aligns with your career goals, it’s important to evaluate several factors that could influence your learning experience and professional growth.

4 Things to Look for in an AI Course That Could Benefit Your Career

1. content quality, applicability, and relevance.

When selecting an AI course, ensure the content covers foundational AI concepts and aligns with your career goals. Look for programs that offer real-world, case-based examples relevant to your industry.

For instance, if your goal is to leverage AI in healthcare, a program with industry-specific examples can provide practical insights directly applicable to your work. In HBS Online’s AI Essentials for Business course, learners are introduced to VideaHealth, a dentistry startup using AI to detect dental conditions on X-rays.

In the course, VideaHealth Co-Founder and CEO Florian Hillen discusses the benefits of using AI for patient diagnostics.

“AI, especially in this diagnostic step—but also later in the treatment planning step—can introduce a more objective, data-driven, and consistent approach toward introducing transparency and trust in this marketplace,” Hillen says in AI Essentials for Business .

High-quality, real-world content delivered through an interactive online platform can make learning AI more relevant to your career goals.

2. Industry Expertise

The instructors’ expertise is crucial to your learning experience. Courses taught by and featuring industry leaders who deeply understand AI’s role in business can provide invaluable insights far beyond textbook knowledge.

When researching courses, research the faculty members’ backgrounds. Instructors with firsthand experience and deep industry knowledge can bridge theory with practice and ensure the course content addresses common challenges and emerging opportunities in the field, making the lessons relevant to your role and today’s ever-evolving business landscape.

This connection between industry expertise and practical application is exemplified by the instructors of HBS Online’s AI Essentials for Business course: renowned HBS professors Karim Lakhani and Marco Iansiti.

“Marco and I recently finished this book called Competing in the Age of AI ,” Lakhani shares in the course. “And we're really pleased to welcome you to our course, which takes the insights of the book and massively expands it by including a ton of voices of leaders from around the world thinking about how AI is changing the nature of business, both business models and operating models.”

By choosing a course led by experts like those at HBS, you can be confident you’re learning from the best in the field.

3. AI Ethics Coursework

In the rush to implement AI technologies, many overlook the need to better understand their ethical considerations . And since this isn’t industry-specific, nearly any business professional can benefit from this relevant coursework.

“It's becoming very fundamental to a whole new generation of leaders across both small and large firms,” Iansiti says in AI Essentials for Business . “The extent to which, as these firms drive this immense scale, scope, and learning, there are all kinds of really important ethical considerations that need to be part of the management, the leadership philosophy from the get-go.”

As a result, the best AI courses should include a strong focus on ethics, equipping learners with the knowledge to effectively navigate these challenges.

One particularly relevant concept with AI in business is algorithmic bias , the systematic discrimination that can occur when AI decision-making is influenced by prejudiced data. According to AI Essentials for Business , that bias results in unfair outcomes like:

• Discriminatory hiring
• Unequal access to resources
• Workplace bias

An effective AI course teaches you strategies for responsible, ethical AI use, offering tips on recognizing and addressing bias, the power of diverse data sets, and enhancing privacy and cybersecurity. This approach ensures fairness, transparency, and safety in your organization’s AI applications and promotes ethical decision-making .

4. Generative AI Insights

As AI capabilities expand, understanding generative AI —a subset of AI that uses generative models to create new content, such as text, images, and simulations—offers business professionals a strategic advantage.

Unlike traditional AI, which often focuses on data analysis and prediction, generative AI provides the tools to innovate, design, and problem-solve in ways previously unimaginable. Business leaders must understand how it can be applied to their industry-specific challenges and opportunities.

For example, in marketing and product development, generative AI can optimize several aspects of a product launch by:

• Creating personalized content: Generative AI can help you tailor marketing materials to specific audience segments based on customer data.
• Designing new products: AI can analyze trends and customer preferences, empowering you to use those insights to inform innovative product features.
• Simulating customer interactions: You can leverage AI to model customer responses to refine your organization’s engagement strategies before launch.

By gaining a deep understanding of these cutting-edge technologies through a comprehensive AI course, you can position yourself and your organization at the forefront of innovation, driving success in a competitive market.

How to Advance Your Career with an AI Certificate

In an era where AI is rapidly transforming industries, selecting the right AI certificate course is crucial for advancing your career . The right program doesn’t just teach you the basics; it equips you with the practical skills and strategic insights to apply AI in ways that drive real business value.

HBS Online’s AI Essentials for Business certificate course can help enhance your career prospects in the age of AI. You’ll develop frameworks to shape your organization’s digital transformation strategy, empowering you to deliver business value, lead AI initiatives, and prepare your business for AI’s impact.

Taught by world-renowned faculty and industry experts, this course offers a comprehensive, accessible, and actionable approach to AI—making it a powerful next step you can take to advance your career.

Are you interested in taking an AI course? Explore AI Essentials for Business —one of our online digital transformation courses —and download our interactive online learning success guide to discover the benefits of online programs and how to prepare.

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