problem solving and python programming syllabus regulation 2017

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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

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SEMESTER 1 LABORATORY (LAB) SYLLABUS ANNA UNIVERSITY REGULATION 2017

Syllabus semester 1 laboratory (lab) anna university regulation 2017, practical labs are listed below:.

• PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY
• PHYSICS LABORATORY

CHEMISTRY LABORATORY

CLICK HERE TO DOWNLOAD THE SYLLABUS OF SEMESTER 1 LAB

GE8161 PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY

Objectives:.

• To write, test, and debug simple Python programs. • To implement Python programs with conditionals and loops. • Use functions for structuring Python programs. • Represent compound data using Python lists, tuples, dictionaries. • Read and write data from/to files in Python.

LIST OF PROGRAMS:

1. Compute the GCD of two numbers. 2. Find the square root of a number (Newton’s method) 3. Exponentiation (power of a number) 4. Find the maximum of a list of numbers 5. Linear search and Binary search 6. Selection sort, Insertion sort 7. Merge sort 8. First n prime numbers 9. Multiply matrices 10. Programs that take command line arguments (word count) 11. Find the most frequent words in a text read from a file 12. Simulate elliptical orbits in Pygame 13. Simulate bouncing ball using Pygame

BS8161 PHYSICS AND CHEMISTRY LABORATORY

Objectives of physics lab bs8161:.

• To introduce different experiments to test basic understanding of physics concepts applied in optics, thermal physics, properties of matter and liquids.

OUTCOMES OF PHYSICS LAB BS8161:

Upon completion of the course, the students will be able to

• apply principles of elasticity, optics and thermal properties for engineering applications.

OBJECTIVES CHEMISTRY LAB SYLLABUS:

• To make the student to acquire practical skills in the determination of water quality parameters through volumetric and instrumental analysis.
• To acquaint the students with the determination of molecular weight of a polymer by viscometery.

OUTCOMES OF CHEMISTRY LAB SYLLABUS:

• The students will be outfitted with hands-on knowledge in the quantitative chemical analysis of water quality related parameters.

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GE8161 Problem Solving and Python Programming Laboratory Lab Manual – Ist Sem

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GE8161 PSPP LAB SYLLABUS

We are providing the GE8161 Problem Solving and Python Programming Laboratory Syllabus and Lab Manual below for your Lab examination success. use our Materials to score good marks in the Lab examination. Best of Luck.

OBJECTIVES:

To write, test, and debug simple Python programs.

To implement Python programs with conditionals and loops. Use functions for structuring Python programs.

Represent compound data using Python lists, tuples, dictionaries. Read and write data from/to files in Python.

LIST OF PROGRAMS:

• Compute the GCD of two numbers.
• Find the square root of a number (Newton’s method)
• Exponentiation (power of a number)
• Find the maximum of a list of numbers
• Linear search and Binary search
• Selection sort, Insertion sort
• First n prime numbers
• Multiply matrices
• Programs that take command line arguments (word count)
• Find the most frequent words in a text read from a file
• Simulate elliptical orbits in Pygame
• Simulate bouncing ball using Pygame

PLATFORM NEEDED

Python 3 interpreter for Windows/Linux

TOTAL: 60 PERIODS

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

Write, test, and debug simple Python programs.

Implement Python programs with conditionals and loops.

Develop Python programs step-wise by defining functions and calling them.

Use Python lists, tuples, dictionaries for representing compound data. Read and write data from/to files in Python.

GE8161 Problem Solving and Python Programming Laboratory Useful Links :

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GE8161 Problem Solving and Python Programming Laboratory syllabus, GE8161 Problem Solving and Python Programming Lab Manual, GE8161 PSPP Lab Syllabus, GE8161 PSPP Lab Manual

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Problem Solving and Python Programming - GE3151, GE8151

Important questions and answers, question paper download, online study material, lecturing notes, assignment, reference, wiki.

Exam Question Papers

Question bank, problem solving and python programming, unit i: computational thinking and problem solving, unit ii: data types, expressions, statements, unit iii: control flow, functions, strings, unit iv: lists, tuples, dictionaries, unit i : algorithmic problem solving, unit ii : data expressions statements, unit iii : control flow functions, unit iv : lists tuples dictionaries, unit v : files modules packages.

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GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING Regulation 2021 Anna University

• Post author By Mr. Padeepz
• Post date October 31, 2021
• 1 Comment on GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING Regulation 2021 Anna University

GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING

GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING Regulation 2021 Anna University free download. PROBLEM SOLVING AND PYTHON PROGRAMMING Syllabus GE3151 pdf download free.

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. GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING 􀁸 To do input/output with files in Python.

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. GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING

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. GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING

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).

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: GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING

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: GE3151 Syllabus PSPP

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 GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING 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.

GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING Click Here To Download

GE3151 Notes PROBLEM SOLVING AND PYTHON PROGRAMMING

GE3151 Important Questions PROBLEM SOLVING AND PYTHON PROGRAMMING

GE3151 Question Bank PROBLEM SOLVING AND PYTHON PROGRAMMING

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1 reply on “GE3151 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING Regulation 2021 Anna University”

Please can you give the whole book to download.

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CS8451 Design and Analysis of Algorithms Syllabus:

CS8451 Design and Analysis of Algorithms Syllabus – Anna University Regulation 2017

OBJECTIVES:

• To learn the algorithm analysis techniques.
• To critically analyze the efficiency of alternative algorithmic solutions for the same problem
• To understand the limitation of algorithm power.
• To understand different algorithm design techniques.

UNIT I INTRODUCTION

Notion of an Algorithm — Fundamentals of Algorithmic Problem Solving — Important Problem Types — Fundamentals of the Analysis of Algorithmic Efficiency –Asymptotic Notations and their properties. Analysis Framework — Empirical analysis — Mathematical analysis for Recursive and Non-recursive algorithms — Visualization

UNIT II BRUTE FORCE AND DIVIDE-AND-CONQUER

Brute Force — Computing an — String Matching — Closest-Pair and Convex-Hull Problems — Exhaustive Search — Travelling Salesman Problem — Knapsack Problem — Assignment problem. Divide and Conquer Methodology — Binary Search — Merge sort — Quick sort — Heap Sort — Multiplication of Large Integers — Closest-Pair and Convex — Hull Problems.

UNIT III DYNAMIC PROGRAMMING AND GREEDY TECHNIQUE

Dynamic programming — Principle of optimality — Coin changing problem, Computing a Binomial Coefficient — Floyd?s algorithm — Multi stage graph — Optimal Binary Search Trees — Knapsack Problem and Memory functions. Greedy Technique — Container loading problem — Prim?s algorithm and Kruskal?s Algorithm — 0/1 Knapsack problem, Optimal Merge pattern — Huffman Trees.

UNIT IV ITERATIVE IMPROVEMENT

The Simplex Method — The Maximum-Flow Problem — Maximum Matching in Bipartite Graphs, Stable marriage Problem.

UNIT V COPING WITH THE LIMITATIONS OF ALGORITHM POWER

Lower — Bound Arguments — P, NP NP- Complete and NP Hard Problems. Backtracking — n-Queen problem — Hamiltonian Circuit Problem — Subset Sum Problem. Branch and Bound — LIFO Search and FIFO search — Assignment problem — Knapsack Problem — Travelling Salesman Problem — Approximation Algorithms for NP-Hard Problems — Travelling Salesman problem — Knapsack problem.

TEXT BOOKS:

1. Anany Levitin, ―Introduction to the Design and Analysis of Algorithms‖, Third Edition, Pearson Education, 2012. 2. Ellis Horowitz, Sartaj Sahni and Sanguthevar Rajasekaran, Computer Algorithms/ C++, Second Edition, Universities Press, 2007.

REFERENCES:

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Browse Course Material

Course info.

• Dr. Ana Bell

Departments

• Electrical Engineering and Computer Science

As Taught In

• Programming Languages

Learning Resource Types

Introduction to cs and programming using python, course meeting times.

Lectures: 2 sessions / week; 1.5 hours / session

Course Prerequisites

No prerequisites.

Course Description

This class is a full-semester version of 6.100A (formerly 6.0001 Introduction to Computer Science and Programming in Python ). The material covered is the same, but the pace is slowed down. Our goal with this course is to give students who have never programmed the time to practice the concepts.

This subject is aimed at students with little to no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, feel justifiably confident in their ability to write simple programs that allow them to accomplish useful goals. The class will use the Python 3 programming language.

• Provide an understanding of the role computation can play in solving problems.
• Help students, including those who do not necessarily plan to major in computer science, feel justifiably confident in their ability to write small programs that allow them to accomplish useful goals.
• Position students so that they can compete for UROPs and excel in subjects like 6.100B and 6.1010.

The textbook is John Guttag’s Introduction to Computation and Programming Using Python, Third Edition, with Application to Computational Modeling and Understanding Data, MIT Press. ISBN: 9780262542364. [ Buy at MIT Press ] The book and the course lectures parallel each other, though there is more detail in the book about some topics. It is available both in hard copy and as an e-book. See the code and errata for the book.

Grading Policy 

• Problem sets: 45%
• Completion of mandatory finger exercises: 10%
• Microquizzes (best 7 out of 10): 45%

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