🐍 Python-S1 — Developer Toolkit & Python Foundations

The first session of the DEPI R5 Professional Data Analyst track: a hands-on introduction to Python built around the mindset of a working data analyst. Every concept is anchored to a realistic scenario — cleaning messy transaction exports, auditing inventory, paginating API results — instead of toy examples.

📓 Main notebook: Python-S1.ipynb

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📖 What's Inside

1. Print Function

• print() basics: single, double, and triple quotes
• Escape characters
• Printing variables alongside text
• 🧪 Challenge: Store Receipt Generator

2. Variables

• Naming rules and reserved keywords
• Inspecting Python's keyword list
• 🧪 Challenge: Monthly Sales Performance Report

3. Input Function

• Capturing dynamic user input
• 🧪 Challenge: Dataset Profile Reporter

4. Python Data Types

• Single-value types: int, float, str, bool
• The empty type: None
• Visual reference: Data Types Big Picture
• 📝 Multiple-choice quick check
• 🧪 Challenge: Raw Customer Transaction Cleaner

5. Strings & Numeric Types

• String methods and cleaning techniques
• Numeric operations, math, and random
• 🧪 Challenge: Annual Revenue Analytics Engine

6. Controlling the Flow

• Boolean expressions & comparison/logical operators
• Visual reference: Python Flow Control
• 🧪 Challenge: Transaction Record Validator

7. Conditional Branching

• if / elif / else statements
• Inline (ternary) if expressions
• match-case (Python 3.10+)
• 🧪 Challenges: Smart Order Processing Engine · Order Summary Badge Generator
• 🏆 Capstone: ShopEasy Analytics — End-of-Day Transaction Audit Pipeline

8. Flow Control with Loops

• Iterable objects: lists, tuples, strings, range()
• for loops and while loops (condition-based & while True)
• Loop control keywords: break, continue, pass
• 🧪 Challenges:
  • Weekly Sales Trend Analyzer
  • Customer Feedback Word & Sentiment Scanner
  • Multi-Store Inventory Restock Auditor
  • API Pagination Simulator
• 🏆 Final Capstone: ShopEasy Analytics — Daily Batch Settlement Engine

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🎯 Learning Approach

Each concept follows the same rhythm:

Theory → Live Demo → 🧪 Real-World Challenge

Challenges are framed as tasks a junior data analyst at ShopEasy Analytics would actually face — cleaning raw exports, validating records, auditing stores, and building settlement pipelines — so the skills map directly onto real analyst work.

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📂 Repo Contents

File	Description
Python-S1.ipynb	Main lecture notebook (the source of truth)
Python-S1.txt	Plain-text export of the lecture
Data Types Big Picture.png	Visual reference for Python data types
Python Flow control.png	Visual reference for flow control
python_data_types_dashboard.html	Supplementary interactive reference

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Part of the DEPI R5 Professional Data Analyst program.