| 1 |
Introduction to Programming & Computer Systems (Python installation, running scripts, using Jupyter notebooks, basic print() and input/output) |
| 2 |
Number Systems, Basic Algorithms, and Problem-Solving Logic (Converting between number systems, writing basic algorithm pseudocode, implementing simple algorithms in Python) |
| 3 |
Variables, Data Types, Constants, and Basic Operations (Working with integers, floats, strings, and booleans; simple arithmetic and assignments in Python) |
| 4 |
Operators and Expressions (Hands-on with arithmetic, logical, comparison, and assignment operators; small expression-based tasks) |
| 5 |
Conditional Statements (Writing decision-based programs; simple calculators, grade checkers) |
| 6 |
Loops (Loop-based exercises: generating patterns, summing sequences, and basic simulations) |
| 7 |
MIDTERM |
| 8 |
Functions and Modular Programming (Writing reusable functions; parameter passing, return values, and modular script design) |
| 9 |
Data Structures I: Lists and Tuples (Hands-on with lists and tuples; indexing, slicing, and basic manipulations) |
| 10 |
Data Structures II: Dictionaries and Sets (Key-value pair manipulation, set operations, and basic data organization tasks) |
| 11 |
File Handling (Reading and writing text files; parsing simple datasets (e.g., CSV)) |
| 12 |
Introduction to Data Science Libraries (Using NumPy arrays and Pandas Series/DataFrames for basic data manipulation etc.) |
| 13 |
Algorithmic Problem Solving for Data Tasks (Implementing small algorithms for data processing, e.g., summarizing, filtering, or transforming datasets) |
| 14 |
Project Presentation (Students present a mini-project using Python for a basic data science problem) |
