📊 Data Science Week 1 of 14 BSc · Y1 S1 ⏱ ~50 min

Week 1: Python Fundamentals & Data Structures

From syntax to data structures, OOP, and scripting patterns — the Python skills every data scientist needs from day one.

University of America

PROG101 — Lecture 1 · BSc Y1 S1

🎬 CC Licensed Lecture

0:00 / —:—— 📺 Creative Commons Licensed

🎯 Learning Objectives

Write Python functions, classes, and modules
Use list/dict comprehensions for concise data processing
Understand Python's data structures and when to use each
Read and write files, handle exceptions, and use virtual environments

Topics Covered This Lecture

Variables, Types & Control Flow

Functions & Lambda

OOP: Classes & Inheritance

File I/O & Exception Handling

📖 Lecture Overview

This first lecture establishes the foundational framework for Python Programming. By the end of this session, you will have the conceptual grounding and practical starting point needed for the rest of the course.

        Why this matters
        From syntax to data structures, OOP, and scripting patterns — the Python skills every data scientist needs from day one. This lecture sets up everything that follows — make sure you understand the core concepts before proceeding to Week 2.
      

Key Concepts

The lecture introduces the four main pillars of this course: Variables, Types & Control Flow, Functions & Lambda, OOP: Classes & Inheritance, File I/O & Exception Handling. Each will be explored in depth over the 14-week curriculum, with hands-on projects reinforcing theory at every stage.

# Quick Start: verify your environment is ready for PROG101
import sys
print(f"Python {sys.version}")

# Check key libraries are installed
try:
    import numpy, pandas, matplotlib
    print("✅ Core libraries ready")
except ImportError as e:
    print(f"❌ Missing: {e} — run: pip install numpy pandas matplotlib")

This Week's Focus

Focus on mastering: Variables, Types & Control Flow and Functions & Lambda. These are the prerequisites for everything in Week 2. The concepts build on each other — do not skip the practice exercises.

📋 Project 1 of 3 50% of Final Grade

PROG101 Project 1: Data Processing CLI Tool

Build a command-line Python tool that reads a CSV file, performs filtering and aggregation based on user arguments, and outputs formatted results. Must include proper error handling.

Python CLI script with argparse
CSV reading and processing with pure Python or pandas
At least 3 data operations (filter, aggregate, sort)
Unit tests for core functions (pytest)

50%

3 Projects

20%

Midterm Exam

30%

Final Exam

📝 Sample Exam Questions

These represent the style and difficulty of questions you'll see on the midterm and final. Start thinking about them now.

Conceptual Short Answer

What is the difference between a Python list and a tuple? When would you prefer each?

Analysis Short Answer

Explain what a generator is and why it is memory-efficient compared to a list.

Applied Code / Proof

Write a Python class `DataRecord` with `__init__`, `__repr__`, and a method `to_dict()`.