GE3151 PROBLEM SOLVING AND PYTHON PROGRAMMING Anna University Syllabus Regulation 21

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GE3151                PROBLEM SOLVING AND PYTHON PROGRAMMING       LTPC 3003                   

 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. 

UNIT I                     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.

UNIT II                                DATA TYPES, EXPRESSIONS, STATEMENTS                      9 

 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                        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. 

UNIT IV                                         LISTS, TUPLES, DICTIONARIES                                 9

 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                               9 

Files and exceptions: 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).

 

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 loops 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. 

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