Course

Python Programming

A 12-week hands-on Python course where you write real code from day one and ship a project to GitHub every single week. Master the fundamentals — variables, control flow, data structures, functions, files, and object-oriented programming — then put them to work: calling live APIs and scraping the web, analyzing real datasets with pandas, automating Excel reports and repetitive tasks, testing with pytest, and building a small FastAPI web service. You finish with a free-choice capstone — a CLI tool, automation suite, data report, or API — presented and defended like a professional, plus a GitHub portfolio of 12 working projects. No prior programming experience required; expect 10–15 hours per week of lessons, labs, and a weekly project.

Module 1Week 1 — Setup & First Programs

  1. Why Python & setting up your machine
    25mFree preview
  2. The terminal & the Python REPL
    25mFree preview
  3. Variables & core types
    30mFree preview
  4. Strings, input & output
    25mFree preview
  5. Lab: install, verify & first script
    60mPracticalFree preview
  6. Lab: Mad Libs & a receipt printer
    75mPracticalFree preview
  7. Weekly project: unit & currency converter
    150mProjectFree preview

Module 2Week 2 — Control Flow

  1. Making decisions: if, elif, else
    30m🔒
  2. Loops: while & for
    30m🔒
  3. Boolean logic, break & continue
    30m🔒
  4. Think before you type: pseudocode
    25m🔒
  5. Lab: number guessing game
    60mPractical🔒
  6. Lab: loop drills
    75mPractical🔒
  7. Weekly project: ATM simulator
    165mProject🔒

Module 3Week 3 — Data Structures

  1. Lists & tuples
    30m🔒
  2. Dictionaries: the most useful structure in Python
    30m🔒
  3. Sets & choosing the right structure
    25m🔒
  4. Slicing & comprehensions
    25m🔒
  5. Lab: shopping cart drills
    70mPractical🔒
  6. Lab: word frequency counter
    60mPractical🔒
  7. Weekly project: contact book
    165mProject🔒

Module 4Week 4 — Functions, Modules & Git

  1. Functions: name your logic
    25m🔒
  2. Scope, defaults & flexible arguments
    25m🔒
  3. Modules, pip & virtual environments
    30m🔒
  4. Git & GitHub: your code, safe and public
    30m🔒
  5. Lab: refactor the ATM into functions
    60mPractical🔒
  6. Lab: your GitHub portfolio begins
    90mPractical🔒
  7. Weekly project: expense splitter
    150mProject🔒

Module 5Week 5 — Files & Errors

  1. Reading & writing files
    30m🔒
  2. CSV: spreadsheets for programs
    25m🔒
  3. JSON: saving structured data
    25m🔒
  4. Exceptions: crash on purpose, recover on purpose
    30m🔒
  5. Lab: notes app & log reader
    75mPractical🔒
  6. Lab: CSV sales report
    75mPractical🔒
  7. Weekly project: persistent expense tracker
    165mProject🔒

Module 6Week 6 — Object-Oriented Programming

  1. Classes & objects: data with behavior attached
    30m🔒
  2. Methods & dunder methods
    30m🔒
  3. Inheritance vs composition
    30m🔒
  4. Dataclasses & when not to use OOP
    25m🔒
  5. Lab: model a shop
    75mPractical🔒
  6. Lab: refactor the expense tracker
    90mPractical🔒
  7. Weekly project: inventory manager
    180mProject🔒

Module 7Week 7 — The Standard Library & Real CLI Tools

  1. datetime & random: time and chance
    28m🔒
  2. The filesystem: os, shutil & pathlib together
    27m🔒
  3. Regular expressions: enough to be dangerous
    30m🔒
  4. argparse: CLIs with flags like a real tool
    25m🔒
  5. Lab: extract phones & prices
    60mPractical🔒
  6. Lab: timestamped backup script
    75mPractical🔒
  7. Weekly project: file organizer CLI
    150mProject🔒

Module 8Week 8 — APIs & Web Scraping

  1. How the web talks: HTTP in plain language
    25m🔒
  2. requests: calling real APIs
    28m🔒
  3. Web scraping with BeautifulSoup
    30m🔒
  4. API keys, secrets & scraping ethics
    26m🔒
  5. Lab: call a public API
    60mPractical🔒
  6. Lab: scrape headlines
    60mPractical🔒
  7. Weekly project: live exchange-rate CLI
    165mProject🔒

Module 9Week 9 — Data Analysis with pandas

  1. Jupyter notebooks & a taste of numpy
    27m🔒
  2. pandas: DataFrames & first look at data
    30m🔒
  3. Cleaning real-world data
    30m🔒
  4. Groupby & charts that answer questions
    28m🔒
  5. Lab: your first notebook
    60mPractical🔒
  6. Lab: clean the messy sales file
    90mPractical🔒
  7. Weekly project: data analysis report
    180mProject🔒

Module 10Week 10 — Automation: Scripts That Save Hours

  1. The automation mindset
    25m🔒
  2. Excel automation with openpyxl
    30m🔒
  3. Sending email & building reports
    30m🔒
  4. Scheduling: run it while you sleep
    25m🔒
  5. Lab: monthly sales report generator
    75mPractical🔒
  6. Lab: schedule a script
    60mPractical🔒
  7. Weekly project: automate an hour of your week
    150mProject🔒

Module 11Week 11 — Shipping Quality Code: Testing, Structure & FastAPI

  1. Debugging like a professional
    30m🔒
  2. Testing with pytest: proof your code works
    30m🔒
  3. Project structure, README & docs that get you hired
    25m🔒
  4. FastAPI: your code as a web service
    30m🔒
  5. Lab: test the expense tracker
    75mPractical🔒
  6. Lab: build a small API
    75mPractical🔒
  7. Weekly project: a tested, documented service
    165mProject🔒

Module 12Week 12 — Capstone: Build, Review & Present

  1. Scoping a capstone that actually ships
    30m🔒
  2. Lab: build sprint
    90mPractical🔒
  3. Lab: peer code review clinic
    90mPractical🔒
  4. Career pathways & what to learn next
    30m🔒
  5. Capstone: build, document & present
    240mProject🔒

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