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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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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.

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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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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.

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Click Here To Download

Lab Manual In Padeepz App – Click Here To Download

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GE8151 – Problem Solving and Python Programming – Regulation 2017 Syllabus

Ge8151 – notes & qp, ge8151 – syllabus, unit i algorithmic problem solving.

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, EXPRESSIONS, STATEMENTS 

Python interpreter and interactive mode; values and types: int, float, boolean, string, and list; variables, expressions, statements, tuple assignment, precedence of operators, comments; modules and functions, function definition and use, flow of execution, parameters and arguments; Illustrative programs: exchange the values of two variables, circulate the values of n variables, distance between two points.

UNIT III CONTROL FLOW, FUNCTIONS 

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: selection sort, insertion sort, mergesort, histogram.

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.

GE3251 – Engineering Graphics – Regulation 2021 Syllabus

Be3252 – basic electrical, electronics and instrumentation engineering [beeie] – regulation 2021 syllabus, ee3251 – electric circuit analysis [eca] – regulation 2021 syllabus.

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GE3171: Problem Solving and Python Programming Laboratory syllabus for Production 2021 regulation

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

Problem Solving and Python Programming Laboratory detailed syllabus for Production Engineering (Production) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the Production 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 Production Engineering 1st Sem scheme and its subjects, do visit Production 1st Sem 2021 regulation scheme . The detailed syllabus of problem solving and python programming laboratory is as follows.

Problem Solving and Python Programming Laboratory

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:

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.

• 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.)
• Python programming using simple statements and expressions (exchange the values of two variables, circulate the values of n variables, distance between two points).
• Scientific problems using Conditionals and Iterative loops. (Number series, Number Patterns, pyramid pattern)
• 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)
• Implementing real-time/technical applications using Sets, Dictionaries. (Language, components of an automobile, Elements of a civil structure, etc.- operations of Sets & Dictionaries)
• Implementing programs using Functions. (Factorial, largest number in a list, area of shape)
• Implementing programs using Strings. (reverse, palindrome, character count, replacing characters)
• Implementing programs using written modules and Python Standard Libraries (pandas, numpy. Matplotlib, scipy)
• Implementing real-time/technical applications using File handling. (copy from one file to another, word count, longest word)
• Implementing real-time/technical applications using Exception handling. (divide by zero error, voters age validity, student mark range validation)
• Exploring Pygame tool.
• Developing a game activity using Pygame like bouncing ball, car race etc.

Course Outcomes:

On completion of the course, students will be able to:

• Develop algorithmic solutions to simple computational problems
• Develop and execute simple Python programs.
• Implement programs in Python using conditionals and loops for solving problems..
• Deploy functions to decompose a Python program.
• Process compound data using Python data structures.
• Utilize Python packages in developing software applications.

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 Production Engineering, 2021 regulation curriculum do visit Production 1st Sem subject syllabuses for 2021 regulation .

For all Production Engineering results, visit Anna University Production all semester results direct link.

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