Introduction to Problem Solving with Computers Using Python Programming

This course will introduce you to problem-solving using programming. Beginning with simple tasks like evaluating mathematical expressions, calculating income-tax, to solving JEE problems (maths, physics, chemistry), we will gradually move on to engineering problems from all branches of engineering (mechanical, electrical, mechatronics, computer). We will focus on building logic, learning design patterns such as modularity, establishing program correctness, debugging, testing, and efficiency.

https://web.stanford.edu/class/cs109/handouts/honorCode.html

• Rule 1: You must not look at solutions that are not your own.
• Rule 2: You must not share your solutions with other students.
• Rule 3: You must indicate on your submission any assistance you received.
• Rule 4: Don't ask an LLM/Google/ChatGpt/Gemini etc. to solve homework for you
• Please be aware: all submissions are subject to automated plagiarism detection
• Discuss ideas together, but do the coding on your own... Perhaps the only way you may fail this course is by engaging in dishonesty

Monday: 9:30 am-10:25 am -- Lecture 1 -- LH300
Thursday: 9:30 am-10:25 am -- Lecture 2 -- LH300

Tuesday: 8:30 am to 9:30 am Tutorial for entire class -- LH300

Labs are in ED1 Tuesday ---> Batch 2 (ED1 Room 320) and Batch 6 (ED1 room 312)
Wednesday ---> Batch 3 (ED1 Room 106) and Batch 5 (ED1 room 312)
Thursday ----> Batch 4 (ED1 Room 106) and Batch 1 (ED1 room 312)

Ed1 room 312 has a capacity of 32. First 32 students on Batches 6,5 and 1 can be seated in ED1 room 312 while others can be seated in TL1 (Ed1 room 106). Eventually, the ED1 room 312 may be used for students that need more help in the respective batches for the day.

Office Hours* During office hours, you can meet the instructor and ask your questions, doubts etc. Friday, ED1 Room 312 (11:30 a.m. - 1:30 p.m.)

After Hours Lab Timings and Volunteers

• What is Computer Science?
• Can all problems be solved by computers? Undecidability, Halting problem
• Algorithms: pseudocode, flowcharts
• Limitations of computers
• Lab: scratch programming

• Types of Programming Languages
• Interpreters vs Compilers
• Flowgorithm
• Conditional Statements: if, if-else, elif
• Loops: for, while
• Why Python?
• Lab: Flowgorithm

• Math for computer scientists: PMI
• Practice with algorithm development
• Imperative model of computing
• Functional model of computing
• Lab: Turtle graphics, game development

• Common File Extensions
  .png, .jpg, .csv, .txt, .py, .ipynb, etc.
• Data Types
  int(), float(), str(), bool(), etc.
• Number Systems
  Binary, Decimal, Hexadecimal, Octal
• ASCII & Unicode
• Type Casting
• Loop controls: break, continue, pass
• range() function
• Nested loops
• Simple pattern printing

• Python installation & syntax
• Python IDEs: Jupyter Notebook, Spyder, VS Code, PyCharm
• Virtual Environment in Python
• Variables and Keywords
• Data types overview
• Operators:
  • Arithmetic
  • Relational
  • Logical
  • Assignment
  • Membership
  • Identity
• input() and print()
• Comments and indentation

• Creating strings and using methods:
  upper(), lower(), find(), replace(), slicing, etc.
• String formatting: f-strings, .format()
• Escape characters & raw strings
• Formatted input

• Defining functions
• Parameters and return values
• Default & keyword arguments
• Variable-length arguments: *args, **kwargs
• Scope: local vs global
• Recursion basics and examples

• List operations and methods
• Nested lists
• Tuples and immutability
• Tuple unpacking
• Memory representation: list vs tuple
• List comprehension

• Set operations: union, intersection, difference
• Set methods
• Dictionary creation, access, and methods
• Looping through dictionaries
• Nested dictionaries
• get(), setdefault(), fromkeys() methods

• List, Set, Dictionary comprehensions
• Shallow Copy vs Deep Copy: copy() and deepcopy()
• Comparison: is vs ==
• Memory allocation: id(), mutability
• del statement

• Built-in functional tools: map(), filter(), reduce()
• Lambda expressions
• Intro to popular libraries: numpy, pandas, matplotlib
• Importing modules: import, from
• Built-in modules: math, random, datetime
• Writing your own module

• Classes and Objects
• __init__ method
• self and instance attributes
• Encapsulation and abstraction
• Inheritance: single, multiple
• Method overriding
• Composition

• open(), read(), write(), close()
• File modes: r, w, a, rb, etc.
• Exception handling: try, except, finally, raise

• Basic commands: ls, cd, pwd, mkdir, rm, touch, cat
• File permissions: chmod, chown
• Piping & redirection
• Navigating folders and environment variables

• Using GitHub Copilot and AI tools
• Introduction to LLMs (Large Language Models)
• Prompt engineering for coding tasks
• Algebra and Numerical Analysis:
  • Automatic Differentiation
  • Integration
• System of Linear Equations:
  • Gaussian Elimination (with code)