problem solving and python programming syllabus regulation 2021 pdf

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[PDF] GE3151 Problem Solving and Python Programming (PSPP) Books, Lecture Notes, 2 marks with answers, Important Part B 16 Marks Questions, Question Bank & Syllabus

Download GE3151 Problem Solving and Python Programming (PSPP) Books Lecture Notes Syllabus Part-A 2 marks with answers GE3151 Problem Solving and Python Programming Important Part-B 16 marks Questions , PDF Books, Question Bank with answers Key, GE3151 Problem Solving and Python Programming Syllabus & Anna University GE3151 Problem Solving and Python Programming Question Papers Collection.

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Download link is provided for Students to download the Anna University GE3151 Problem Solving and Python Programming Syllabus Question Bank Lecture Notes Part A 2 marks with answers & Part B 16 marks Question Bank with answer , Anna University Question Paper Collection, All the materials are listed below for the students to make use of it and get good (maximum) marks with our study materials.

“GE3151 Problem Solving and Python Programming Notes, Lecture Notes, Previous Years Question Papers “

“GE3151 Problem Solving and Python Programming Important 16 marks Questions with Answers”

“GE3151 Problem Solving and Python Programming Important 2 marks & 16 marks Questions with Answers”

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“GE3151 Problem Solving and Python Programming Syllabus, Local Author Books, Question Banks”

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GE3151 Problem Solving and Python Programming – Study Materials – Details

GE3151 Problem Solving and Python Programming (PSPP) “R2021 – SYLLABUS”

GE3151 PROBLEM SOLVING AND PYTHON PROGRAMMING

UNIT I COMPUTATIONAL THINKING AND PROBLEM SOLVING

Fundamentals of Computing – Identification of Computational Problems -Algorithms, building blocks of algorithms (statements, state, control flow, functions), notation (pseudo code, flow chart, programming language), algorithmic problem solving, simple strategies for developing algorithms (iteration, recursion). Illustrative problems: find minimum in a list, insert a card in a list of sorted cards, guess an integer number in a range, Towers of Hanoi.

UNIT II DATA TYPES, EXPRESSIONS, STATEMENTS

Python interpreter and interactive mode, debugging; values and types: int, float, boolean, string, and list; variables, expressions, statements, tuple assignment, precedence of operators, comments; Illustrative programs: exchange the values of two variables, circulate the values of n variables, distance between two points.

UNIT III CONTROL FLOW, FUNCTIONS, STRINGS

Conditionals: Boolean values and operators, conditional (if), alternative (if-else), chained conditional (if-elif-else); Iteration: state, while, for, break, continue, pass; Fruitful functions: return values, parameters, local and global scope, function composition, recursion; Strings: string slices, immutability, string functions and methods, string module; Lists as arrays. Illustrative programs: square root, gcd, exponentiation, sum an array of numbers, linear search, binary search.

UNIT IV LISTS, TUPLES, DICTIONARIES

Lists: list operations, list slices, list methods, list loop, mutability, aliasing, cloning lists, list parameters; Tuples: tuple assignment, tuple as return value; Dictionaries: operations and methods; advanced list processing – list comprehension; Illustrative programs: simple sorting, histogram, Students marks statement, Retail bill preparation.

UNIT V FILES, MODULES, PACKAGES

Files and exception: text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules, packages; Illustrative programs: word count, copy file, Voter’s age validation, Marks range validation (0-100).

  TEXT BOOKS:

• Allen B. Downey, “Think Python : How to Think like a Computer Scientist”, 2nd Edition, O’Reilly Publishers, 2016.
• Karl Beecher, “Computational Thinking: A Beginner's Guide to Problem Solving and programming”, 1st Edition, BCS Learning & Development Limited, 2017.

REFERENCES:

• Paul Deitel and Harvey Deitel, “Python for Programmers”, Pearson Education, 1st Edition,
• G Venkatesh and Madhavan Mukund, “Computational Thinking: A Primer for Programmers and Data Scientists”, 1st Edition, Notion Press, 2021.
• John V Guttag, "Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data‘‘, Third Edition, MIT Press 2021
• Eric Matthes, “Python Crash Course, A Hands – on Project Based Introduction to Programming”, 2nd Edition, No Starch Press, 2019.
• https://www.python.org/
• 6. Martin C. Brown, “Python: The Complete Reference”, 4th Edition, Mc-Graw Hill,2018.

DOWNLOAD LINK

Anna University GE3151 Problem Solving and Python Programming Books Question Banks Lecture Notes Syllabus GE3151 Problem Solving and Python Programming Part A 2 Marks with Answers Part – B 16 Marks Questions with Answers & Anna University GE3151 Problem Solving and Python Programming Question Paper Collection and Local Author Books.

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1st Sem , IEM

GE3151: Problem Solving and Python Programming syllabus for IEM 2021 regulation

• Updated on Mar 22, 2023
• By InI Labs TN

Problem Solving and Python Programming detailed syllabus for Industrial Engineering & Management (IEM) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the IEM students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Industrial Engineering & Management 1st Sem scheme and its subjects, do visit IEM 1st Sem 2021 regulation scheme . The detailed syllabus of problem solving and python programming is as follows.

Problem Solving and Python Programming

Course Objectives:

Download the istudy app for all syllabus and other updates..

COMPUTATIONAL THINKING AND PROBLEM SOLVING 9 Fundamentals of Computing – Identification of Computational Problems -Algorithms, building blocks of algorithms (statements, state, control flow, functions), notation (pseudo code, flow chart, programming language), algorithmic problem solving, simple strategies for developing algorithms (iteration, recursion). Illustrative problems: find minimum in a list, insert a card in a list of sorted cards, guess an integer number in a range, Towers of Hanoi.

CONTROL FLOW, FUNCTIONS, STRINGS 9 Conditionals: Boolean values and operators, conditional (if), alternative (if-else), chained conditional (if-elif-else); Iteration: state, while, for, break, continue, pass; Fruitful functions: return values, parameters, local and global scope, function composition, recursion; Strings: string slices, immutability, string functions and methods, string module; Lists as arrays. Illustrative programs: square root, gcd, exponentiation, sum an array of numbers, linear search, binary search.

FILES, MODULES, PACKAGES 9 Files and exception: text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules, packages; Illustrative programs: word count, copy file, Voters age validation, Marks range validation (0-100).

Course Outcomes:

Text books:.

• Allen B. Downey, Think Python : How to Think like a Computer Scientist, 2nd Edition, OReilly Publishers, 2016.
• Karl Beecher, Computational Thinking: A Beginner’s Guide to Problem Solving and programming, 1st Edition, BCS Learning & Development Limited, 2017.

Reference Books:

• Paul Deitel and Harvey Deitel, Python for Programmers, Pearson Education, 1st Edition, 2021.
• G Venkatesh and Madhavan Mukund, Computational Thinking: A Primer for Programmers and Data Scientists, 1st Edition, Notion Press, 2021.
• John V Guttag, “Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data, Third Edition, MIT Press 2021
• Eric Matthes, Python Crash Course, A Hands – on Project Based Introduction to Programming, 2nd Edition, No Starch Press, 2019.
• https://www.python.org/
• Martin C. Brown, Python: The Complete Reference, 4th Edition, Mc-Graw Hill,2018.

For detailed syllabus of all other subjects of Industrial Engineering & Management, 2021 regulation curriculum do visit IEM 1st Sem subject syllabuses for 2021 regulation .

For all Industrial Engineering & Management results, visit Anna University IEM all semester results direct link.

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Problem Solving and Python Programming

Ge3151 1st semester | 2021 regulation, tag: [ 1st semester common to all dept 2021 regulation ].

2021 regulation - 1st year, 1st semester paper for all department including EEE, ECE, MECH, CIVIL, CES, IT, etc. Subject Code: GE3151, Subject Name: Problem Solving and Python Programming, Batch: 2021, 2022, 2023, 2024. Institute: Anna University Affiliated Engineering College, TamilNadu. This page has Problem Solving and Python Programming study material, notes, semester question paper pdf download, important questions, lecture notes.

PDF Download Links

• GE3151 Problem Solving and Python Programming Reg 2017 Nov Dec 2019 Question Paper PDF Download
• GE3151 Problem Solving and Python Programming Reg 2017 Nov Dec 2021 Question Paper PDF Download
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• GE3151 Problem Solving and Python Programming Reg 2021 unit I (2 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit II (2 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit III (2 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit V (2 Mark Question & Answer) PDF Download
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• GE3151 Problem Solving and Python Programming Reg 2021 unit I (13 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit II (13 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit III (13 Mark Question & Answer) PDF Download
• GE3151 Problem Solving and Python Programming Reg 2021 unit V (13 Mark Question & Answer) PDF Download
• Computational Thinking and Problem Solving
• Fundamentals of Computing
• Identification of Computational Problems
• Building Blocks of Algorithm
• Algorithmic Problem Solving
• Simple Strategies for Developing Algorithms
• Illustrative Problems
• Anna University Two Marks Questions & Answers
• Multiple Choice Questions and Answers
• Data Types, Expressions, Statements
• Introduction to Python
• How to Write and Execute Python Program
• Concept of Python Interpreter and Compiler
• Interactive and Script Modes
• Values and Types
• Variables, Expressions and Statements
• Tuple Assignment, Comments
• Indentation, String Operations
• Illustrative Python Programs
• Multiple Choice Questions
• Control Flow, Functions, Strings
• Boolean Values
• Input and Output
• Conditional Statements
• Fruitful Functions
• Lists as arrays
• Lists, Tuples, Dictionaries
• Dictionaries
• Advanced List Processing - List Comprehension
• Files, Modules, Packages
• Command Line Arguments
• Errors and Exceptions
• Two Marks Questions with Answers
• Laboratory Python Programs Set 1
• Laboratory Python Programs Set 2
• Laboratory Python Programs Set 3
• Laboratory Python Programs Set 4

Unit I: Computational Thinking and Problem Solving Computational Thinking and Problem Solving Fundamentals of Computing Identification of Computational Problems Algorithms Building Blocks of Algorithm Notation Algorithmic Problem Solving Simple Strategies for Developing Algorithms Illustrative Problems Anna University Two Marks Questions & Answers Multiple Choice Questions and Answers Unit II: Data Types, Expressions, Statements Data Types, Expressions, Statements Introduction to Python How to Write and Execute Python Program Translator Concept of Python Interpreter and Compiler Interactive and Script Modes Debugging Values and Types Variables, Expressions and Statements Tuple Assignment, Comments Indentation, String Operations Functions Illustrative Python Programs Anna University Two Marks Questions & Answers Multiple Choice Questions Unit III: Control Flow, Functions, Strings Control Flow, Functions, Strings Boolean Values Operators Input and Output Conditional Statements Iteration Fruitful Functions Recursion Strings Lists as arrays Illustrative Python Programs Anna University Two Marks Questions & Answers Multiple Choice Questions Unit IV: Lists, Tuples, Dictionaries Lists, Tuples, Dictionaries Lists Tuples Dictionaries Advanced List Processing - List Comprehension Illustrative Python Programs Anna University Two Marks Questions & Answers Multiple Choice Questions Unit V: Files, Modules, Packages Files, Modules, Packages Files Command Line Arguments Errors and Exceptions Modules Packages Two Marks Questions with Answers Multiple Choice Questions Laboratory Programs Laboratory Python Programs Set 1 Laboratory Python Programs Set 2 Laboratory Python Programs Set 3 Laboratory Python Programs Set 4 PDF Download Links

PROBLEM SOLVING AND PYTHON PROGRAMMING

COURSE OBJECTIVES:

To understand the basics of algorithmic problem solving.

• To learn to solve problems using Python conditionals and loops.

• To define Python functions and use function calls to solve problems.

• To use Python data structures - lists, tuples, dictionaries to represent complex data.

• To do input/output with files in Python.

COMPUTATIONAL THINKING AND PROBLEM SOLVING

Fundamentals of Computing - Identification of Computational Problems -Algorithms, building blocks of algorithms (statements, state, control flow, functions), notation (pseudo code, flow chart, programming language), algorithmic problem solving, simple strategies for developing algorithms (iteration, recursion). Illustrative problems: find minimum in a list, insert a card in a list of sorted cards, guess an integer number in a range, Towers of Hanoi.

DATA TYPES, EXPRESSIONS, STATEMENTS WLEDGE

Python interpreter and interactive mode, debugging; values and types: int, float, boolean, string, and list; variables, expressions, statements, tuple assignment, precedence of operators, comments; Illustrative programs: exchange the values of two variables, circulate the values of n variables, distance between two points.

CONTROL FLOW, FUNCTIONS, STRINGS

Conditionals: Boolean values and operators, conditional (if), alternative (if-else), chained conditional (if- elif-else); Iteration: state, while, for, break, continue, pass; Fruitful functions: return values, parameters, local and global scope, function composition, recursion; Strings: string slices, immutability, string functions and methods, string module; Lists as arrays. Illustrative programs: square root, gcd, exponentiation, sum an array of numbers, linear search, binary search.

LISTS, TUPLES, DICTIONARIES

Lists: list operations, list slices, list methods, list loop, mutability, aliasing, cloning lists, list parameters; Tuples: tuple assignment, tuple as return value; Dictionaries: operations and methods; advanced list processing list comprehension; Illustrative programs: simple sorting, histogram, Students marks statement. Retail bill preparation.

FILES, MODULES, PACKAGES

Files and exception: text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules, packages; Illustrative programs: word count, copy file, Voter's age validation, Marks range validation (0-100).

TOTAL: 45 PERIODS

COURSE OUTCOMES:

Upon completion of the course, students will be able to

CO1: Develop algorithmic solutions to simple computational problems.

CO2: Develop and execute simple Python programs.

CO3: Write simple Python programs using conditionals and looping for solving problems.

CO4: Decompose a Python program into functions.

CO5: Represent compound data using Python lists, tuples, dictionaries etc.

CO6: Read and write data from/to files in Python programs.

TEXT BOOKS:

1. Allen B. Downey, "Think Python: How to Think like a Computer Scientist", 2nd Edition, O'Reilly Publishers, 2016.

2. Karl Beecher, "Computational Thinking: A Beginner's Guide to Problem Solving and programming", 1st Edition, BCS Learning & Development Limited, 2017.

REFERENCES:

1. Paul Deitel and Harvey Deitel, "Python for Programmers", Pearson Education, 1st Edition, 2021.

2. G Venkatesh and Madhavan Mukund, "Computational Thinking: A Primer for Programmers and Data Scientists", 1st Edition, Notion Press, 2021.

3. John V Guttag, "Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data", Third Edition, MIT Press 2021

4. Eric Matthes, "Python Crash Course, A Hands-on Project Based Introduction to Programming", 2nd Edition, No Starch Press, 2019.

5. https://www.python.org/

6. Martin C. Brown, "Python: The Complete Reference", 4th Edition, Mc-Graw Hill, 2018.

Problem Solving and Python Programming: Unit I: Computational Thinking and Problem Solving,, Problem Solving and Python Programming: Unit II: Data Types, Expressions, Statements,, Problem Solving and Python Programming: Unit III: Control Flow, Functions, Strings,, Problem Solving and Python Programming: Unit IV: Lists, Tuples, Dictionaries,, Problem Solving and Python Programming: Unit V: Files, Modules, Packages,, Problem Solving and Python Programming: Laboratory Programs 1st Semester Common to all Dept 2021 Regulation : GE3151 1st Semester | 2021 Regulation Problem Solving and Python Programming

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GE3151 Problem Solving and Python Programming Question Papers 2021 Regulation

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GE3151 PSPP Question Paper

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GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University

• Post author By Mr. Padeepz
• Post date October 31, 2021
• No Comments on GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University free download. PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Syllabus GE3171 pdf download free.

COURSE OBJECTIVES: 􀁸 To understand the problem solving approaches. 􀁸 To learn the basic programming constructs in Python. 􀁸 To practice various computing strategies for Python-based solutions to real world problems. 􀁸 To use Python data structures – lists, tuples, dictionaries. 􀁸 To do input/output with files in Python.

EXPERIMENTS: GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Note: The examples suggested in each experiment are only indicative. The lab instructor is expected to design other problems on similar lines. The Examination shall not be restricted to the sample experiments listed here. 1. Identification and solving of simple real life or scientific or technical problems, and developing flow charts for the same. (Electricity Billing, Retail shop billing, Sin series, weight of a motorbike, Weight of a steel bar, compute Electrical Current in Three Phase AC Circuit, etc.) 2. Python programming using simple statements and expressions (exchange the values of two variables, circulate the values of n variables, distance between two points). 3. Scientific problems using Conditionals and Iterative loops. (Number series, Number Patterns, pyramid pattern)

4. Implementing real-time/technical applications using Lists, Tuples. (Items present in a library/Components of a car/ Materials required for construction of a building –operations of list & tuples) 5. Implementing real-time/technical applications using Sets, Dictionaries. (Language, components of an automobile, Elements of a civil structure, etc.- operations of Sets &  Dictionaries) GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY 6. Implementing programs using Functions. (Factorial, largest number in a list, area of shape) 7. Implementing programs using Strings. (reverse, palindrome, character count, replacing characters) 8. Implementing programs using written modules and Python Standard Libraries (pandas,  numpy. Matplotlib, scipy) 9. Implementing real-time/technical applications using File handling. (copy from one file to  another, word count, longest word) 10. Implementing real-time/technical applications using Exception handling. (divide by zero error, voter’s age validity, student mark range validation) 11. Exploring Pygame tool. 12. Developing a game activity using Pygame like bouncing ball, car race etc.

TOTAL: 60 PERIODS

COURSE OUTCOMES:

On completion of the course, students will be able to: CO1: Develop algorithmic solutions to simple computational problems CO2: Develop and execute simple Python programs. CO3: Implement programs in Python using conditionals and loops for solving problems. CO4: Deploy functions to decompose a Python program. CO5: Process compound data using Python data structures. CO6: Utilize Python packages in developing software applications.

TEXT BOOKS: GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY

1. Allen B. Downey, “Think Python : How to Think like a Computer Scientist”, 2nd Edition, O’Reilly Publishers, 2016. 2. Karl Beecher, “Computational Thinking: A Beginner’s Guide to Problem Solving and Programming”, 1st Edition, BCS Learning & Development Limited, 2017.

REFERENCES:

1. Paul Deitel and Harvey Deitel, “Python for Programmers”, Pearson Education, 1st Edition, 2021. 2. G Venkatesh and Madhavan Mukund, “Computational Thinking: A Primer for Programmers and Data Scientists”, 1st Edition, Notion Press, 2021. 3. John V Guttag, “Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data‘‘, Third Edition, MIT Press, 2021 4. Eric Matthes, “Python Crash Course, A Hands – on Project Based Introduction to Programming”, 2nd Edition, No Starch Press, 2019. 5. https://www.python.org/ 6. Martin C. Brown, “Python: The Complete Reference”, 4th Edition, Mc-Graw Hill, 2018.

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