Course Introduction
This project-driven bootcamp teaches Python by doing: one small project per day for 100 days. The course moves quickly from fundamentals to professional-level workflows, covering web development, automation, data science, machine learning basics, GUIs, games, and deployment. It’s ideal for learners who prefer hands-on practice and want a large, portfolio-ready body of work by the end. The instructor-led format, abundant resources, and step-by-step projects make it friendly for beginners while still valuable for intermediate coders building applied skills.
Instructor: De anegla Yu
Last Updated: December 2025
Duration: ~52 hours on-demand video (56.5h total length listed)
Rating: 4.7 / 5 (403,344 ratings)
Learners: 1,702,689 students
Languages: English, Arabic (Auto)
Certificate: Included
Access: Lifetime on mobile & TV
Extras: 23 coding exercises • 230 articles • 164 downloadable resources
Price Note: ₹519 %9.99 (₹3,109 $45 original)
Key Highlights
- Project-first approach: 100 projects in 100 days to build a portfolio
- Broad coverage: web apps, automation, data science, ML intro, games, GUIs
- Teaches in-demand libraries: Pandas, NumPy, Flask, Selenium, BeautifulSoup, Scikit-learn, Plotly, Matplotlib
- Practical deployment & tooling: PyCharm, Jupyter, Google Colab, and deployment tips
- Large, active student community and extensive downloadable resources
- Designed for daily practice — excellent for habit-building and rapid skill growth
What You Will Learn
Python Fundamentals & Core Programming
- Syntax, control flow, functions, data structures (lists, dicts, sets, tuples)
- File I/O, string manipulation, error handling, modules and packages
Automation & Web Scraping
- Web automation and scraping using Selenium and BeautifulSoup
- HTTP requests, parsing HTML, and extracting real-world data
Web Development & Deployment
- Building web apps with Flask (routes, templates, forms)
- Basics of deployment and serving small web apps
Data Science & Machine Learning Foundations
- Data manipulation with Pandas and numeric computing with NumPy
- Data visualization using Matplotlib and Plotly
- Introductory machine learning workflows with Scikit-learn
Games, GUIs & Desktop Apps
- Building simple games (e.g., Blackjack, Pong, Snake) to apply logic and OOP
- Creating desktop GUIs using Python frameworks
Portfolio Development & Best Practices
- One small project per day to assemble a 100-project portfolio
- Code organization, version control tips, and project-readme practices
- Preparing projects for interviews and job applications
Frequently Asked Questions (FAQ)
Q — Do I need prior coding experience to take this course?
A — No. The course is beginner-friendly and starts with the basics, but it moves quickly — daily practice is important.
Q — What software will I use?
A — The course shows how to use free tools like PyCharm, Jupyter Notebooks, and Google Colab; no paid software required.
Q — Will I finish 100 full projects?
A — The curriculum is designed as 100 focused, achievable projects. Many are small by design so you can complete one per day and build momentum.
Q — Is this course suitable for job preparation?
A — Yes. Building 100 projects gives you a broad portfolio; combined with the taught libraries and deployment tips, it’s useful for entry-level roles.
Q — Does the course cover data science and machine learning?
A — It covers core data tools (Pandas, NumPy, plotting) and introductory ML with Scikit-learn — enough to start practical experiments and portfolios.
Q — How much time should I commit daily?
A — The recommended pace is ~1 hour per day (hence “100 Days of Code”), but many students spend more time to deepen specific projects.
Why This Course Stands Out
The course’s strength is its sheer volume of project-based practice. Completing dozens of small, real projects instills practical fluency far faster than passively watching videos. The mix of web, automation, data, and games ensures you touch multiple Python use-cases, making it ideal for learners who want both breadth and a demonstrable portfolio.
