Overview of Python and its applications in engineering-Basic Syntax and Data Type: Variables, data types, and basic operations- Conditional statements and loop.
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: 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 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).
NumPy: Introduction to NumPy arrays and operations- Data manipulation with Pandas-Data visualization using Matplotlib.-Data Import and Export: Handling CSV, Excel, and other data formats.
Reference Book:
1. Paul Deitel and Harvey Deitel, “Python for Programmersâ€, Pearson Education, 1st Edition,2021. 2. G Venkatesh and MadhavanMukund, “Computational Thinking: A Primer for Programmers and Data Scientistsâ€, 1st Edition, Notion Press, 2021.
Text Book:
1. Allen B. Downey, “Think Python: How to Think like a Computer Scientistâ€, 2nd Edition, O’Reilly Publishers, 2016. 2. Karl Beecher, “Computational Thinking: A Beginner's Guide to Problem Solving and Programmingâ€, 1st Edition, BCS Learning & Development Limited, 2017.
