Is Uncodemy Good for Learning Python in 2026?

Level 1 — Syllabus (Weeks 1-4):
Week 1 — Setup & Syntax
  • Installing Python & VS Code / PyCharm
  • Variables, data types, operators
  • Input, output, f-strings
Week 2 — Control Flow & Loops
  • if / elif / else
  • for and while loops
  • break, continue, else on loops
Week 3 — Functions & Data Structures
  • Defining and calling functions
  • Arguments, return values
  • Lists, tuples, sets, dictionaries
Week 4 — Files, Errors & Debugging
  • Reading and writing files
  • try/except/finally
  • Debugging with print and pdb

Level 1 — Hands-on Labs:
Lab 1: Print "Hello, world!" and take user input
Lab 2: Build a simple calculator
Lab 3: Write a number-guessing game
Lab 4: Build a to-do list CLI app
Lab 5: Read a CSV file and print totals
Lab 6: Build a simple expense tracker with try/except

Deliverable:
□ 5 small programs pushed to GitHub
□ A CLI app you can demo
□ A clean Python project folder structure

Level 2 — Syllabus (Weeks 5-8):
Week 5 — Object-Oriented Programming
  • Classes and objects
  • Inheritance, encapsulation, polymorphism
  • Dunder methods and properties
Week 6 — Modules, Packages & Environments
  • import statements and module structure
  • pip and virtual environments
  • Standard library tour
Week 7 — APIs & Web Requests
  • HTTP basics
  • requests library, JSON parsing
  • Error handling for unreliable APIs
Week 8 — Intro to Data with NumPy & Pandas
  • NumPy arrays and operations
  • Pandas Series and DataFrames
  • Reading and cleaning CSV data

Level 2 — Hands-on Labs:
Lab 1: Build a bank account class with inheritance
Lab 2: Create your own Python package
Lab 3: Set up a virtual environment and requirements.txt
Lab 4: Build a weather CLI app with the requests library
Lab 5: Analyse a public CSV dataset with Pandas
Lab 6: Build a small report generator that exports a PDF

Deliverable:
□ A reusable Python package on GitHub
□ A data analysis notebook with insights
□ An API-powered mini app

Level 3 — Syllabus (Weeks 9-12):
Week 9 — Automation with Python
  • File and folder automation
  • Web scraping with BeautifulSoup
  • Scheduling with cron and schedule
Week 10 — Data Science / ML Basics
  • Pandas for data analysis
  • Matplotlib and Seaborn for visuals
  • scikit-learn: regression & classification
Week 11 — Web & API Development
  • Flask or FastAPI basics
  • Routing, requests, JSON responses
  • Deployment to a cloud host
Week 12 — Capstone & Interview Prep
  • Build an end-to-end project
  • Portfolio page and GitHub cleanup
  • Mock interviews and feedback

Level 3 — Hands-on Labs:
Lab 1: Automate a weekly report from local files
Lab 2: Scrape a website and save the data to CSV
Lab 3: Build a small ML model for a public dataset
Lab 4: Deploy a Flask API that returns predictions
Lab 5: Build a data dashboard in Python
Lab 6: Complete a capstone project end-to-end

Deliverable:
□ 3 documented projects on GitHub
□ A deployed web API
□ A capstone project you can demo in interviews

Daily Tracker:
Date: _________
☐ Attended class / watched recording
☐ Wrote Python code today (30+ min)
☐ Completed today's lab
☐ Pushed code to GitHub
☐ Read documentation or an article
☐ Reviewed yesterday's concepts
☐ Planned tomorrow

Today's Key Learnings:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

Weekly Tracker:
Week: _____ (Level 1 / 2 / 3)

Goals for this week:
1. 
2. 
3. 

Daily Progress:
Mon: ☐☐☐
Tue: ☐☐☐
Wed: ☐☐☐
Thu: ☐☐☐
Fri: ☐☐☐
Sat: ☐☐☐
Sun: ☐☐☐

Weekly Wins:
1. 
2. 
3. 

Challenges & Solutions:
1. 
2. 

Next Week's Goals:
1. 
2. 
3. 

Monthly Tracker:
Month: _____ (1 / 2 / 3)

Monthly Goal:
_______________________________________________

Week 1 Progress: ☐☐☐
Week 2 Progress: ☐☐☐
Week 3 Progress: ☐☐☐
Week 4 Progress: ☐☐☐

Monthly Wins:
1. 
2. 
3. 

Python Concepts Mastered:
1. 
2. 
3. 

Projects Completed:
1. 
2. 
3. 

Labs Completed: _____
Mock Interviews: _____
Portfolio Readiness: _____%

Next Month's Goal:
_______________________________________________