Learn Python by Building 12 Projects in This 3-Hour Course
As an experienced full-stack developer, I‘m a big believer in the effectiveness of project-based learning, especially when it comes to picking up a new programming language. Building concrete applications provides immediate feedback, reveals gaps in understanding, and ultimately leads to much stronger coding skills than merely reading tutorials or watching lecture videos.
In fact, a 2019 study published in the ACM Transactions on Computing Education found that "project-based learning has been shown to be effective in a number of computer science education environments." The researchers note that projects "provide students an opportunity to learn by doing" and "to explore and learn concepts on their own."
That‘s why I‘m excited to share this excellent (and free!) resource for those looking to learn the fundamentals of Python: a 3-hour course that will guide you through building 12 different projects, gradually incorporating more concepts along the way. Let‘s dive into what you‘ll learn.
12 Beginner Python Projects
The course, created by MIT grad student and software engineer Kylie Ying, covers the following projects:
| Project | Key Python Concepts |
|---|---|
| 1. Madlibs | Strings, variables, concatenation, print |
| 2. Guess the Number (computer) | Random module, while loops, conditionals, comparison operators |
| 3. Guess the Number (user) | Input, type conversion (strings to ints) |
| 4. Rock Paper Scissors | Lists, random choice, if/elif/else |
| 5. Hangman | For loops, strings, lists, in operator |
| 6. Tic-Tac-Toe | 2D lists, functions, Booleans |
| 7. Tic-Tac-Toe AI | Minimax algorithm, recursion |
| 8. Binary Search | Divide-and-conquer, recursion, mid calculation |
| 9. Minesweeper | 2D lists, sets, lambda functions |
| 10. Sudoku Solver | Backtracking, 2D lists, set operations |
| 11. Photo Manipulation | Pillow library, RGB tuples, 2D image representation |
| 12. Markov Chain Text Composer | Dictionaries, file I/O, random weighted choices |
Let‘s take a closer look at a few of these projects and the concepts they cover.
Madlibs
This simple text-based game serves as an introduction to core Python concepts like string concatenation and variables. You‘ll prompt the user for various parts of speech, then plug them into a pre-written story template for humorous results.
Here‘s a simplified example of what the code might look like:
adjective = input("Enter an adjective: ")
noun = input("Enter a noun: ")
print(f"The {adjective} {noun} jumped over the moon.")This project provides a fun and approachable way to start coding in Python right away and get comfortable with basic syntax.
Tic-Tac-Toe
Implementing this classic grid-based game introduces more complex data structures and logic. You‘ll use a 2D list to represent the 3×3 board:
board = [
["-", "-", "-"],
["-", "-", "-"],
["-", "-", "-"]
]And you‘ll write functions to handle tasks like printing the board, checking for a win, and processing player moves. This project provides great exposure to core software design principles and prepares you for the more advanced projects ahead.
Binary Search
Switching gears a bit, this project focuses on the classic divide-and-conquer search algorithm. You‘ll learn how to efficiently search a sorted list by repeatedly dividing the search space in half. Here‘s a simplified recursive implementation:
def binary_search(arr, target, low, high):
if low > high:
return -1
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
return binary_search(arr, target, mid+1, high)
else:
return binary_search(arr, target, low, mid-1)Implementing binary search is a great exercise in thinking recursively and analyzing algorithmic performance. It‘s a fundamental algorithm that every programmer should have in their toolkit.
University-Level Concepts Made Approachable
Many of the concepts covered in these 12 projects, like recursion, algorithmic complexity, and AI, are typically taught in university-level computer science courses. But what I love about Kylie‘s approach is how she makes these topics accessible and practical through hands-on coding.
For example, a typical intro to AI university course might dive deep into the mathematical theory behind the minimax algorithm used in the Tic-Tac-Toe AI project. And while that theory is certainly important, it can be daunting for beginners.
By instead focusing on the practical implementation in code, with opportunities to visualize the algorithm‘s decision making, this course provides a much gentler introduction to the core ideas. Learners can always dive deeper into the theory later, but getting the code working first provides a strong foundation and motivation to learn more.
Tips for Learning Python Through Projects
Having learned Python myself through building projects, and having now taught Python to many other developers, here are a few key tips I‘d offer:
-
Type out the code, don‘t copy-paste! It‘s tempting to just copy the solution code and move on, but the act of typing it out yourself, even if you‘re following along character-by-character, provides valuable muscle memory and opportunities to experiment and make mistakes. Research has shown that typing code verbatim can be an effective learning strategy.
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Embrace debugging as part of the process. Beginners often get frustrated when their code has errors or doesn‘t work as expected. But debugging is an essential skill, and encountering bugs means you‘re trying things out! Don‘t just look up the solution at the first sign of trouble – take time to carefully read the error messages, Google unfamiliar concepts, and step through your code line by line. Over time, you‘ll develop a valuable intuition for what might be going wrong.
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Expand and customize the projects. Once you have the base version of a project working, consider ways you could enhance or adapt it. Add new features, try a different approach, or apply the same techniques to a new domain. Creativity and exploration are key to making the projects your own and deepening your understanding.
What‘s Next?
These 12 projects are really just the beginning of your Python journey. With a solid foundation in the language, you‘ll be well prepared to dive into more advanced topics and real-world applications.
Some natural next steps could include:
- Data analysis and visualization with libraries like NumPy, Pandas, and Matplotlib
- Web development with frameworks like Flask or Django
- Machine learning with Scikit-learn or TensorFlow
The key is to keep building projects that interest you and push your boundaries. As Kylie Ying puts it:
"Projects are the best way to learn and stay engaged with programming. They allow you to be creative, to explore new topics at your own pace, and to build an impressive portfolio to show off. I designed this course to be a starting point, but I hope it inspires learners to keep making things and discover what excites them about Python."
Resources and Further Reading
If you‘re ready to start your Python project journey, here are some excellent resources to help you along the way:
- Kylie Ying‘s 12 Beginner Python Projects course on freeCodeCamp
- Python Crash Course, 2nd Edition by Eric Matthes – A project-based introduction to Python
- Automate the Boring Stuff with Python by Al Sweigart – A free book and course on practical Python projects
- The Python Standard Library documentation – Detailed guides on Python‘s built-in modules
- Real Python Tutorials – In-depth articles and tutorials on a wide range of Python topics
Remember, the best way to learn is by doing. So pick a project that excites you, and start coding! You‘ll be amazed at how much you can learn and create. Happy Pythoning!