tools for data science final assignment

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Tools for Data Science

This course is part of multiple programs. Learn more

This course is part of multiple programs

Taught in English

Some content may not be translated

Instructors: Aije Egwaikhide +2 more

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(28,285 reviews)

Recommended experience

Beginner level

Anyone can complete this course in a self-paced manner without any prior programming or Data Science experience. 

What you'll learn

Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools 

Utilize languages commonly used by data scientists like Python, R, and SQL 

Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features  

Create and manage source code for data science using Git repositories and GitHub. 

Skills you'll gain

• Data Science
• Python Programming
• Jupyter notebooks

Details to know

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12 quizzes, 1 assignment

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There are 7 modules in this course

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them.

You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Overview of Data Science Tools

In this module, you will learn about the different types and categories of tools that data scientists use and popular examples of each. You will also become familiar with Open Source, Cloud-based, and Commercial options for data science tools.

What's included

6 videos 3 readings 2 quizzes 1 plugin

6 videos • Total 39 minutes

• Course Introduction • 4 minutes • Preview module
• Categories of Data Science Tools • 7 minutes
• Open Source Tools for Data Science - Part 1 • 7 minutes
• Open Source Tools for Data Science - Part 2 • 5 minutes
• Commercial Tools for Data Science • 6 minutes
• Cloud Based Tools for Data Science • 8 minutes

3 readings • Total 25 minutes

• Learning goals for the course • 10 minutes
• Model Development • 10 minutes
• Module 1 Summary • 5 minutes

2 quizzes • Total 40 minutes

• Practice Quiz - Data Science Tools • 10 minutes
• Graded Quiz - Data Science Tools • 30 minutes

1 plugin • Total 15 minutes

• Open source tool board • 15 minutes

Languages of Data Science

For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.

5 videos 1 reading 2 quizzes

5 videos • Total 21 minutes

• Languages of Data Science • 2 minutes • Preview module
• Introduction to Python • 4 minutes
• Introduction to R Language • 4 minutes
• Introduction to SQL • 4 minutes
• Other Languages for Data Science • 6 minutes

1 reading • Total 2 minutes

• Module 2 Summary • 2 minutes
• Practice Quiz - Languages • 10 minutes
• Graded Quiz - Languages • 30 minutes

Packages, APIs, Data Sets, and Models

In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.

6 videos 1 reading 2 quizzes 2 plugins

6 videos • Total 33 minutes

• Libraries for Data Science • 5 minutes • Preview module
• Application Programming Interfaces (APIs) • 4 minutes
• Data Sets - Powering Data Science • 5 minutes
• Sharing Enterprise Data - Data Asset eXchange • 4 minutes
• Machine Learning Models – Learning from Models to Make Predictions • 7 minutes
• The Model Asset eXchange • 6 minutes

1 reading • Total 3 minutes

• Module 3 Summary • 3 minutes

2 quizzes • Total 42 minutes

• Practice Quiz - Libraries, APIs, Data Sets, Models • 12 minutes
• Graded Quiz - Libraries, APIs, Data Sets, Models • 30 minutes

2 plugins • Total 20 minutes

• Additional Sources of Datasets • 5 minutes
• Reading: Getting started with the Model Asset eXchange and the Data Asset Exchange • 15 minutes

Jupyter Notebooks and JupyterLab

With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.

6 videos 1 reading 2 quizzes 3 app items 2 plugins

6 videos • Total 21 minutes

• Introduction to Jupyter Notebooks • 3 minutes • Preview module
• Getting Started with Jupyter • 4 minutes
• Jupyter Kernels • 2 minutes
• Jupyter Architecture • 2 minutes
• Additional Anaconda Jupyter Environments • 5 minutes
• Additional Cloud Based Jupyter Environments • 4 minutes
• Module 4 Summary • 2 minutes
• Practice Quiz - Jupyter Notebooks and Jupyter Lab • 10 minutes
• Graded Quiz - Jupyter Notebooks and JupyterLab • 30 minutes

3 app items • Total 40 minutes

• Hands-on Lab: Getting Started with Jupyter Notebooks • 10 minutes
• Hands-on Lab: Using Markdown in Jupyter Notebooks • 15 minutes
• Hands-on Lab: Working with Files in Jupyter Notebooks • 15 minutes
• (Optional): Hands-on Lab: Download & Install Anaconda on Windows • 15 minutes
• Jupyter Notebooks on the Internet • 5 minutes

RStudio & GitHub

R is a statistical programming language and is a powerful tool for data processing and manipulation. This module will start with an introduction to R and RStudio. You will learn about the different R visualization packages and how to create visual charts using the plot function. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. While there are many distributed versioning systems, Git is amongst the most popular ones. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.

7 videos 2 readings 3 quizzes 5 app items 3 plugins

7 videos • Total 29 minutes

• Introduction to R and RStudio • 3 minutes • Preview module
• Plotting in RStudio • 3 minutes
• Overview of Git/GitHub • 4 minutes
• Introduction to GitHub • 4 minutes
• GitHub Repositories • 4 minutes
• GitHub - Getting Started • 3 minutes
• GitHub - Working with Branches • 5 minutes

2 readings • Total 13 minutes

• Module 5 Summary • 3 minutes
• Glossary • 10 minutes

3 quizzes • Total 50 minutes

• Practice Quiz - RStudio • 10 minutes
• Practice Quiz - GitHub • 10 minutes
• Graded Quiz - RStudio & GitHub • 30 minutes

5 app items • Total 220 minutes

• R Basics with RStudio • 15 minutes
• Getting started with RStudio and Installing packages • 60 minutes
• Creating Data Visualizations using ggplot • 60 minutes
• Plotting with RStudio • 60 minutes
• [Optional] Getting Started with Branches using Git Commands • 25 minutes

3 plugins • Total 55 minutes

• Optional Reading: Download & Install R and RStudio • 15 minutes
• Hands-on Lab: Getting Started with GitHub • 20 minutes
• Hands-On Lab: Branching and Merging (Web UI) • 20 minutes

Create and Share your Jupyter Notebook

In this module, you will work on a final project to demonstrate some of the skills learned in the course. You will also be tested on your knowledge of various components and tools in a Data Scientist's toolkit learned in the previous modules.

1 assignment 1 peer review 1 app item 1 plugin

1 assignment • Total 36 minutes

• Final Exam • 36 minutes

1 peer review • Total 60 minutes

• Submit Your Work and Grade Your Peers • 60 minutes

1 app item • Total 60 minutes

• Hands-on Lab: Create your Jupyter Notebook • 60 minutes

1 plugin • Total 30 minutes

• Final Assignment Instructions: Create and Share Your Jupyter Notebook • 30 minutes

[Optional] IBM Watson Studio

Watson Studio is a collaborative platform for the data science community and is used by Data Analysts, Data Scientists, Data Engineers, Developers, and Data Stewards to analyze data and construct models. In this module, you will learn about Watson Studio and IBM Cloud Pak for data as a service. Then you will create an IBM Watson Studio service and a project in Watson Studio. After creating the project, you will create a Jupyter notebook and load a data file. You will also explore the different templates and kernels in a Jupyter notebook. Finally, you will connect your Watson Studio account to GitHub and publish the notebook in GitHub. Note: This part of the course is optional and is not a mandatory requirement to complete the lab provided in this week of the course.

5 videos 1 reading 1 quiz 1 app item 2 plugins

• Introduction to Watson Studio • 7 minutes • Preview module
• Optional: Creating an account on IBM Watson Studio • 3 minutes
• Jupyter Notebooks in Watson Studio - Part 1 • 5 minutes
• Jupyter Notebooks in Watson Studio - Part 2 • 2 minutes
• Linking GitHub to Watson Studio • 2 minutes
• Summary • 2 minutes

1 quiz • Total 15 minutes

• Practice Quiz - Watson Studio • 15 minutes
• (Optional)Obtain IBM Cloud Feature Code and Activate Trial Account • 60 minutes

2 plugins • Total 35 minutes

• Creating a Watson Studio Project with Jupyter Notebooks • 15 minutes
• Assignment using Watson Studio • 20 minutes

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. For more information about IBM visit: www.ibm.com

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Learner reviews

Showing 3 of 28285

28,285 reviews

Reviewed on Apr 26, 2021

Great course, I would really encourage everyone to go through, however videos about Jupyter Notebook or other tools were so fast I wasn't able to remember all the information. Anyway great course.

Reviewed on Nov 18, 2020

Some of the lab assignments had instructions that didn't line up with how the programs actually worked. This was particularly the case for modular flow where auto-numerics seemed impossible to use.

Reviewed on Aug 28, 2023

It's been a pleasure for doing this course at IBM via Coursera. Excellent experience on this course. Projects are good to do and peer to peer submission is good. I like to go for other course on it.

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Frequently asked questions

When will i have access to the lectures and assignments.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

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Practice Exams

Course Notes

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Career Guides

A selection of practice exams that will test your current data science knowledge. Identify key areas of improvement to strengthen your theoretical preparation, critical thinking, and practical problem-solving skills so you can get one step closer to realizing your professional goals.

Excel Mechanics

Imagine if you had to apply the same Excel formatting adjustment to both Sheet 1 and Sheet 2 (i.e., adjust font, adjust fill color of the sheets, add a couple of empty rows here and there) which contain thousands of rows. That would cost an unjustifiable amount of time. That is where advanced Excel skills come in handy as they optimize your data cleaning, formatting and analysis process and shortcut your way to a job well-done. Therefore, asses your Excel data manipulation skills with this free practice exam.  

Formatting Excel Spreadsheets

Did you know that more than 1 in 8 people on the planet uses Excel and that Office users typically spend a third of their time in Excel. But how many of them use the popular spreadsheet tool efficiently? Find out where you stand in your Excel skills with this free practice exam where you are a first-year investment banking analyst at one of the top-tier banks in the world. The dynamic nature of your position will test your skills in quick Excel formatting and various Excel shortcuts 

Hypothesis Testing

Whenever we need to verify the results of a test or experiment we turn to hypothesis testing. In this free practice exam you are a data analyst at an electric car manufacturer, selling vehicles in the US and Canada. Currently the company offers two car models – Apollo and SpeedX.  You will need to download a free Excel file containing the car sales of the two models over the last 3 years in order find out interesting insights and  test your skills in hypothesis testing. 

Confidence Intervals

Confidence Intervals refers to the probability of a population parameter falling between a range of certain values. In this free practice exam, you lead the research team at a portfolio management company with over $50 billion dollars in total assets under management. You are asked to compare the performance of 3 funds with similar investment strategies  and are given a table with the return of the three portfolios over the last 3 years. You will have to use the data to answer questions that will test your knowledge in confidence intervals. 

Fundamentals of Inferential Statistics

While descriptive statistics helps us describe and summarize a dataset, inferential statistics allows us to make predictions based off data. In this free practice exam, you are a data analyst at a leading statistical research company. Much of your daily work relates to understanding data structures and processes, as well as applying analytical theory to real-world problems on large and dynamic datasets. You will be given an excel dataset and will be tested on normal distribution, standardizing a dataset, the Central Limit Theorem among other inferential statistics questions.   

Fundamentals of Descriptive Statistics

Descriptive statistics helps us understand the actual characteristics of a dataset by generating summaries about data samples. The most popular types of descriptive statistics are measures of center: median, mode and mean. In this free practice exam you have been appointed as a Junior Data Analyst at a property developer company in the US, where you are asked to evaluate the renting prices in 9 key states. You will work with a free excel dataset file that contains the rental prices and houses over the last years.

Jupyter Notebook Shortcuts

In this free practice exam you are an experienced university professor in Statistics who is looking to upskill in data science and has joined the data science apartment. As on of the most popular coding environments for Python, your colleagues recommend you learn Jupyter Notebook as a beginner data scientist. Therefore, in this quick assessment exam you are going to be tested on some basic theory regarding Jupyter Notebook and some of its shortcuts which will determine how efficient you are at using the environment. 

Intro to Jupyter Notebooks

Jupyter is a free, open-source interactive web-based computational notebook. As one of the most popular coding environments for Python and R, you are inevitably  going to encounter Jupyter at some point in you data science journey, if you have not already. Therefore, in this free practice exam you are a professor of Applied Economics and Finance who is learning how to use Jupyter. You are going to be tested on the very basics of the Jupyter environment like how to set up the environment and some Jupyter keyboard shortcuts. 

Black-Scholes-Merton Model in Python

The Black Scholes formula is one of the most popular financial instruments used in the past 40 years. Derived by Fisher, Black Myron Scholes and Robert Merton in 1973, it has become the primary tool for derivative pricing. In this free practice exam, you are a finance student whose Applied Finance is approaching and is asked to perform the Black-Scholes-Merton formula in Python  by working on a dataset containing Tesla’s stock prices for the period between mid-2010 and mid-2020.  

Python for Financial Analysis

In a heavily regulated industry like fintech, simplicity and efficiency is key. Which is why Python is the preferred choice for programming language over the likes of Java or C++. In this free practice exam you are a university professor of Applied Economics and Finance, who is focused on running regressions and applying the CAPM model on the NASDAQ and The Coca-Cola Company Dataset for the period between 2016 and 2020 inclusive. Make sure to have the following packages running to complete your practice test: pandas, numpy, api, scipy, and pyplot as plt. 

Python Finance

Python has become the ideal programming language for the financial industry, as more and more hedge funds and large investment banks are adopting this general multi-purpose language to solve their quantitative problems. In this free practice exam on Python Finance, you are part of the IT team of a huge company, operating in the US stock market, where you are asked to analyze the performance of three market indices. The packages you need to have running are numpy, pandas and pyplot as plt.   

Machine Learning with KNN

KNN is a popular supervised machine learning algorithm that is used for solving both classification and regression problems. In this free practice exam, this is exactly what you are going to be asked to do, as you are required to create 2 datasets for 2 car dealerships in Jupyter Notebook, fit the models to the training data, find the set of parameters that best classify a car, construct a confusion matrix and more.

Excel Functions

The majority of data comes in spreadsheet format, making Excel the #1 tool of choice for professional data analysts. The ability to work effectively and efficiently in Excel is highly desirable for any data practitioner who is looking to bring value to a company. As a matter of fact, being proficient in Excel has become the new standard, as 82% of middle-skill jobs require competent use of the productivity software. Take this free Excel Functions practice exam and test your knowledge on removing duplicate values, transferring data from one sheet to another, rand using the VLOOKUP and SUMIF function.

Useful Tools in Excel

What Excel lacks in data visualization tools compared to Tableau, or computational power for analyzing big data compared to Python, it compensates with accessibility and flexibility. Excel allows you to quickly organize, visualize and perform mathematical functions on a set of data, without the need for any programming or statistical skills. Therefore, it is in your best interest to learn how to use the various Excel tools at your disposal. This practice exam is a good opportunity to test your excel knowledge in the text to column functions, excel macros, row manipulation and basic math formulas.

Excel Basics

Ever since its first release in 1985, Excel continues to be the most popular spreadsheet application to this day- with approximately 750 million users worldwide, thanks to its flexibility and ease of use. No matter if you are a data scientist or not, knowing how to use Excel will greatly improve and optimize your workflow. Therefore, in this free Excel Basics practice exam you are going to work with a dataset of a company in the Fast Moving Consumer Goods Sector as an aspiring data analyst and test your knowledge on basic Excel functions and shortcuts.

A/B Testing for Social Media

In this free A/B Testing for Social Media practice exam, you are an experienced data analyst who works at a new social media company called FilmIt. You are tasked with the job of increasing user engagement by applying the correct modifications to how users move on to the next video. You decide that the best approach is by conducting a A/B test in a controlled environment. Therefore, in order to successfully complete this task, you are going to be tested on statistical significance, 2 tailed-tests and choosing the success metrics.

Fundamentals of A/B Testing

A/B Testing is a powerful statistical tool used to compare the results between two versions of the same marketing asset such as a webpage or email in a controlled environment. An example of A/B testing is when Electronic Arts created a variation version of the sales page for the popular SimCity 5 simulation game, which performed 40% better than the control page. Speaking about video games, in this free practice test, you are a data analyst who is tasked with the job to conduct A/B testing for a game developer. You are going to be asked to choose the best way to perform an A/B test, identify the null hypothesis, choose the right evaluation metrics, and ultimately increase revenue through in-game ads.

Introduction to Data Science Disciplines

The term “Data Science” dates back to the 1960s, to describe the emerging field of working with large amounts of data that drives organizational growth and decision-making. While the essence has remained the same, the data science disciplines have changed a lot over the past decades thanks to rapid technological advancements. In this free introduction to data science practice exam, you will test your understanding of the modern day data science disciplines and their role within an organization.

Advanced SQL

In this free Advanced SQL practice exam you are a sophomore Business student who has decided to focus on improving your coding and analytical skills in the areas of relational database management systems. You are given an employee dataset containing information like titles, salaries, birth dates and department names, and are required to come up with the correct answers. This free SQL practice test will evaluate your knowledge on MySQL aggregate functions , DML statements (INSERT, UPDATE) and other advanced SQL queries.

Most Popular Practice Exams

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Top 100 Data Science Project Ideas For Final Year

Are you a final year student diving into the world of data science, seeking inspiration for your final project? Look no further! In this blog, we’ll explore a variety of engaging and practical data science project ideas for final year that are perfect for showcasing your skills and creativity. Whether you’re interested in analyzing data trends, building machine learning models, or delving into natural language processing, we’ve got you covered. Let’s dive in!

What is Data Science?

Table of Contents

Data science is a multidisciplinary field that combines various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. At its core, data science involves the use of statistical analysis, machine learning, data mining, and data visualization to uncover patterns, trends, and correlations within datasets.

In simpler terms, data science is about turning raw data into actionable insights. It involves collecting, cleaning, and organizing data, analyzing it to identify meaningful patterns or relationships, and using those insights to make informed decisions or predictions.

Data science encompasses a wide range of applications across industries and domains, including but not limited to:

• Business: Analyzing customer behavior, optimizing marketing strategies, and improving operational efficiency.
• Healthcare: Predicting patient outcomes, diagnosing diseases, and personalized medicine.
• Finance: Fraud detection, risk management, and algorithmic trading.
• Technology: Natural language processing, image recognition, and recommendation systems.
• Environmental Science: Climate modeling, predicting natural disasters, and analyzing environmental data.

In summary, data science is a powerful discipline that leverages data-driven approaches to solve complex problems, drive innovation, and generate value in various fields and industries.

It plays a crucial role in today’s data-driven world, enabling organizations to make better decisions, improve processes, and create new opportunities for growth and development.

How to Select Data Science Project Ideas For Final Year?

Selecting the right data science project idea for your final year is crucial as it can shape your learning experience, showcase your skills to potential employers, and contribute to solving real-world problems. Here’s a step-by-step guide on how to select data science project ideas for your final year:

• Understand Your Interests and Strengths

Reflect on your interests within the field of data science. Are you passionate about healthcare, finance, social media, or environmental issues? Consider your strengths as well. 

Are you proficient in programming languages like Python or R? Do you have experience with statistical analysis, machine learning, or data visualization? Identifying your interests and strengths will help narrow down project ideas that align with your skills and passions.

• Consider the Impact

Think about the impact you want your project to have. Do you aim to address a specific problem or challenge in society, industry, or academia?

Consider the potential beneficiaries of your project and how it can contribute to positive change. Projects with a clear and measurable impact are often more compelling and rewarding.

• Assess Data Availability

Check the availability of relevant datasets for your project idea. Are there publicly available datasets that you can use for analysis? Can you collect data through web scraping, APIs, or surveys?

Ensure that the data you plan to work with is reliable, relevant, and adequately sized to support your analysis and modeling efforts.

• Define Clear Objectives

Clearly define the objectives of your project. What do you aim to accomplish? Are you exploring trends, building predictive models, or developing new algorithms?

Establishing clear objectives will guide your project’s scope, methodology, and evaluation criteria.

• Explore Project Feasibility

Evaluate the feasibility of your project idea given the resources and time constraints of your final year.

Consider factors such as data availability, computational requirements, and the complexity of the techniques you plan to use. Choose a project idea that is challenging yet achievable within your timeframe and resources.

• Seek Inspiration and Guidance

Look for inspiration from existing data science projects, research papers, and industry case studies. Attend workshops, conferences, or webinars related to data science to stay updated on emerging trends and technologies.

Seek guidance from your professors, mentors, or industry professionals who can provide valuable insights and feedback on your project ideas.

• Brainstorm and Refine

Brainstorm multiple project ideas and refine them based on feedback, feasibility, and alignment with your interests and goals.

Consider interdisciplinary approaches that combine data science with other fields such as healthcare, finance, or environmental science. Iterate on your ideas until you find one that excites you and meets the criteria outlined above.

• Plan for Iterative Development

Recognize that data science projects often involve iterative development and refinement.

Plan to iterate on your project as you gather new insights, experiment with different techniques, and incorporate feedback from stakeholders. Embrace the iterative process as an opportunity for continuous learning and improvement.

By following these steps, you can select a data science project idea for your final year that is engaging, impactful, and aligned with your interests and aspirations. Remember to stay curious, persistent, and open to exploring new ideas throughout your project journey.

Exploratory Data Analysis Projects

• Analysis of demographic trends using census data
• Social media sentiment analysis
• Customer segmentation for marketing strategies
• Stock market trend analysis
• Crime rates and patterns in urban areas

Machine Learning Projects

• Healthcare outcome prediction
• Fraud detection in financial transactions
• E-commerce recommendation systems
• Housing price prediction
• Sentiment analysis for product reviews

Natural Language Processing (NLP) Projects

• Text summarization for news articles
• Topic modeling for large text datasets
• Named Entity Recognition (NER) for extracting entities from text
• Social media comment sentiment analysis
• Language translation tools for multilingual communication

Big Data Projects

• IoT data analysis
• Real-time analytics for streaming data
• Recommendation systems using big data platforms
• Social network data analysis
• Predictive maintenance for industrial equipment

Data Visualization Projects

• Interactive COVID-19 dashboard
• Geographic information system (GIS) for spatial data analysis
• Network visualization for social media connections
• Time-series analysis for financial data
• Climate change data visualization

Healthcare Projects

• Disease outbreak prediction
• Patient readmission rate prediction
• Drug effectiveness analysis
• Medical image classification
• Electronic health record analysis

Finance Projects

• Stock price prediction
• Credit risk assessment
• Portfolio optimization
• Fraud detection in banking transactions
• Financial market trend analysis

Marketing Projects

• Customer churn prediction
• Market segmentation analysis
• Brand sentiment analysis
• Ad campaign optimization
• Social media influencer identification

E-commerce Projects

• Product recommendation systems
• Customer lifetime value prediction
• Market basket analysis
• Price elasticity modeling
• User behavior analysis

Education Projects

• Student performance prediction
• Dropout rate analysis
• Personalized learning recommendation systems
• Educational resource allocation optimization
• Student sentiment analysis

Environmental Projects

• Air quality prediction
• Climate change impact analysis
• Wildlife conservation modeling
• Water quality monitoring
• Renewable energy forecasting

Social Media Projects

• Trend detection
• Fake news detection
• Influencer identification
• Social network analysis
• Hashtag sentiment analysis

Retail Projects

• Inventory management optimization
• Demand forecasting
• Customer segmentation for targeted marketing
• Price optimization

Telecommunications Projects

• Network performance optimization
• Fraud detection
• Call volume forecasting
• Subscriber segmentation analysis

Supply Chain Projects

• Inventory optimization
• Supplier risk assessment
• Route optimization
• Supply chain network analysis

Automotive Projects

• Predictive maintenance for vehicles
• Traffic congestion prediction
• Vehicle defect detection
• Autonomous vehicle behavior analysis
• Fleet management optimization

Energy Projects

• Predictive maintenance for equipment
• Energy consumption forecasting
• Renewable energy optimization
• Grid stability analysis
• Demand response optimization

Agriculture Projects

• Crop yield prediction
• Pest detection
• Soil quality analysis
• Irrigation optimization
• Farm management systems

Human Resources Projects

• Employee churn prediction
• Performance appraisal analysis
• Diversity and inclusion analysis
• Recruitment optimization
• Employee sentiment analysis

Travel and Hospitality Projects

• Demand forecasting for hotel bookings
• Customer sentiment analysis for reviews
• Pricing strategy optimization
• Personalized travel recommendations
• Destination popularity prediction

Embarking on data science projects in their final year presents students with an excellent opportunity to apply their skills, gain practical experience, and make a tangible impact.

Whether it’s exploring demographic trends, building predictive models, or visualizing complex datasets, these projects offer a platform for innovation and learning.

By undertaking these data science project ideas for final year, final year students can hone their data science skills and prepare themselves for a successful career in this rapidly evolving field.

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6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
• Submission form