100 Days of Code Udemy Review 2026
100 Days of Code Udemy Review 2026
Angela Yu's "100 Days of Code: The Complete Python Pro Bootcamp" is the best-selling Python course on Udemy with over 1.3 million students enrolled. The premise: complete one Python project per day for 100 days, covering the full range from beginner fundamentals to professional applications.
In 2026, it remains one of the most comprehensive and beginner-friendly Python courses available. Here's what's inside, what works, what doesn't, and who it's actually right for.
Quick Verdict
Buy it (when on sale for $15). It's the closest thing to a Python bootcamp at a fraction of the cost. The curriculum is comprehensive, regularly updated, Angela's teaching style is exceptional, and the project-first approach produces real skills. The 60+ hour runtime is daunting but justified. Skip the last 10–15 days if you're time-constrained — they cover advanced data science topics that most beginners don't need immediately.
Course Overview
| Detail | Info |
|---|---|
| Instructor | Dr. Angela Yu |
| Platform | Udemy |
| Price | $84.99 list / typically $14.99–$19.99 on sale |
| Total length | 60+ hours (video) |
| Projects | 100 projects across 100 days |
| Level | Beginner to intermediate (Day 1 assumes zero Python knowledge) |
| Last updated | 2025 (actively maintained) |
| Student rating | 4.7/5 from 400,000+ reviews |
What You Learn (Day-by-Day Structure)
The course is organized into beginner, intermediate, intermediate+, and advanced sections, with days grouped by topic clusters.
Days 1–14: Beginner Python
- Variables and data types
- String manipulation
- Conditional logic (if/elif/else)
- Randomness and the random module
- Loops (for, while)
- Functions and code reuse
- Debugging basics
Projects: Tip calculator, BMI calculator, password generator, Caesar cipher, blackjack game
The beginner section is gentle and genuinely accessible to people with zero programming experience. Angela builds concepts in small increments and explains not just how to code, but why specific approaches work.
Days 15–31: Intermediate Python
- Local and global scope
- Object-oriented programming (classes, methods, inheritance)
- File I/O
- Error handling
- Working with files and directories
- Third-party APIs and JSON
Projects: Coffee machine OOP project, quiz game, higher-lower game, Turtle graphics projects, mail merge generator
Day 17–18 (OOP) is a significant jump for beginners. Angela handles the transition well, but this is where many learners hit their first real wall. If you slow down here and invest in the OOP concepts, the rest of the course becomes much more approachable.
Days 32–58: Intermediate+
- Sending emails with Python
- SMS automation
- Web scraping (BeautifulSoup, Selenium)
- REST APIs and authentication
- Python decorators
- Flask web development
- RESTful APIs with Flask
- Authentication (Flask-Login)
Projects: Automated birthday emailer, stock news alert, habit tracker with Pixela API, web scraping projects, Flask blog, Flask authentication app
This section is the most immediately marketable. Web scraping, API automation, and Flask are skills that directly apply to freelance and entry-level work. The Selenium projects produce a hands-on browser automation skill set that many employers explicitly look for.
Days 59–80: Advanced
- Tkinter GUI development
- Database integration (SQLite, SQLAlchemy)
- Data science introduction (pandas, matplotlib)
- REST APIs with Flask and SQLAlchemy
Projects: Desktop GUI apps, blog website with database backend, RESTful API from scratch
The Tkinter section is useful for understanding GUI concepts but Tkinter itself is dated — most modern Python desktop apps use PyQt or modern web frameworks instead of Tkinter. This section is worth scanning but not the highest-ROI part of the course.
Days 81–100: Professional Portfolios and Data Science
- Pandas and NumPy
- Matplotlib and Seaborn
- Plotly and data visualization
- Google Trends analysis
- Regression and Scikit-learn intro
Projects: Data analysis projects, portfolio-grade projects with professional README files
The data science section is an introduction, not a comprehensive treatment. If data science is your primary goal, you'll need to supplement with Kaggle Learn or a dedicated data science course. But for learners doing web development or automation, the data science days are optional — cover them if they're relevant to your goal.
What Angela Yu Does Better Than Anyone
Project-first teaching: Every concept is introduced in the context of a project you're building. You don't learn about OOP abstractly — you apply it to build a working game. This approach builds retention and makes the material feel relevant.
Production-quality video: The course is professionally produced with screen recordings, animated explanations, and clear audio. The teaching environment is Replit (browser-based) which removes setup friction for beginners.
Accessibility: Angela paces explanations carefully and assumes no prior knowledge on Day 1. She doesn't talk down to learners, but she also doesn't skip steps that beginners need.
Updates: The course was updated in 2024 and 2025 to replace outdated libraries and projects. When Tweepy (Twitter API) changed, the social media automation projects were updated. When YouTube data APIs changed, those projects were refreshed. Not all instructors maintain their courses this diligently.
What Could Be Better
100 days is aspirational, not realistic. "100 Days" implies 100 calendar days if you do one per day. In practice, most learners take 6–12 months at 3–5 days per week. The framing creates unrealistic expectations about timeline.
Some days are significantly harder than others. Day 17 (OOP) and Day 59 (Tkinter) involve larger conceptual jumps than surrounding days. The course doesn't always signal when these difficulty spikes are coming.
Late-course data science coverage is brief. Days 81–100 introduce pandas, Matplotlib, and Plotly, but 15 days isn't enough to build genuine data analyst skills. Treat these days as an introduction and supplement if data work is your goal.
Replit-only in early days. Angela uses Replit for the first 50+ days. This removes local setup friction, which is good for beginners. But you'll need to set up a local Python environment eventually, and the transition isn't explicitly guided in the course.
Who This Course Is For
Best fit:
- Complete beginners with zero programming experience
- Career changers who want a comprehensive introduction to Python across multiple application areas
- Learners who know they stick with structured courses better than piecing together free resources
- Anyone who wants a course that covers web development AND data AND automation in Python before choosing a specialization
Not the best fit:
- People who already know Python basics and want to go deeper in one area (data science, Django, ML)
- Learners who prefer terse, documentation-style teaching over conversational instruction
- Those who want maximum coverage of data science specifically (supplement with Kaggle or a dedicated data science course)
Price and Value Assessment
The list price of $84.99 is irrelevant — Udemy runs sales so frequently that this course is almost always available for $14.99–$19.99. Wait for a sale (they run multiple times per month).
At $15, a course that covers:
- Web scraping
- API automation
- Flask web development
- Basic data science
- 100 real projects
...is exceptional value. A comparable bootcamp experience costs $8,000–$18,000.
Alternative comparison:
| Option | Cost | Hours | Projects | Instructor Quality |
|---|---|---|---|---|
| 100 Days of Code (Angela Yu) | $15 | 60+ hrs | 100 | Excellent |
| Jose Portilla Python Bootcamp (Udemy) | $15 | 25 hrs | ~20 | Excellent |
| Python for Everybody (Coursera, paid) | $49/month | ~30 hrs | ~15 | Very good |
| freeCodeCamp Python | Free | 50+ hrs | ~10 | Good |
| Coding bootcamp (in-person) | $15,000 | 400+ hrs | 5–10 | Varies |
Angela Yu's course sits at the intersection of low cost, high content quality, and high project volume. The freeCodeCamp Python path is the main free alternative with comparable depth, but Angela's production quality and teaching style are significantly better for learners who need motivation and clear explanation.
How to Get the Most Out of This Course
- Don't just code-along. After each project, build a variation of it from scratch without the tutorial. This is the most important thing you can do.
- Don't skip the debugging exercises. Angela includes debugging challenges throughout. These are where real problem-solving skills form.
- Skip Days 60–75 if you're not targeting desktop GUIs. Tkinter is less relevant for most career paths.
- Do Days 60–80 if you're targeting web development — Flask + SQLAlchemy is directly applicable.
- Add TryHackMe or Kaggle projects alongside if you're targeting a specific role.
Final Rating
| Category | Score |
|---|---|
| Curriculum breadth | 5/5 |
| Teaching quality | 5/5 |
| Project quantity and quality | 5/5 |
| Beginner accessibility | 5/5 |
| Currency / up-to-date content | 4/5 |
| Value for money | 5/5 |
| Overall | 4.8/5 |
Bottom Line
100 Days of Code: The Complete Python Pro Bootcamp is the best first Python course available in 2026 for most beginners. The project-first structure, Angela's teaching quality, and the breadth of coverage — from automation to web development to data science — make it a comprehensive introduction to Python's real-world applications.
Buy it on sale for $15. Work through at least the first 60 days. Build variations of every project from scratch. That routine will make you a functional Python developer faster than any other $15 investment available.
See our how to learn Python guide for context on where this course fits in a complete learning path, or our best Python courses guide for a side-by-side comparison of every major option.
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