Hey there! As a data whiz, I know you‘re always looking to add more tools to your arsenal. And NumPy is one tool you definitely want to master. Lucky for you, I‘ve put together this jam-packed guide on all the best NumPy learning resources out there. Read on to uncover courses, books, tutorials and insider tips to go from NumPy novice to absolute expert!
Why Should You Learn NumPy?
Before we jump into the resources, let me quickly explain what makes NumPy so incredibly useful:
NumPy enables fast mathematical analysis of arrays. The arrays you create with NumPy are much faster than regular Python lists for numerical operations. We‘re talking up to 100x faster thanks to NumPy‘s vectorization superpowers!
This array-based computing provides the foundation for most data science and machine learning workflows. Here are some examples of how NumPy can be leveraged:
- Data cleaning, analysis and feature engineering pipelines
- Complex statistical models that need array math
- Computer vision and image processing algorithms
- Physics simulations with large multidimensional arrays
In fact, popular packages like Pandas, SciPy, Matplotlib and scikit-learn are built using NumPy under the hood. That‘s why gaining NumPy proficiency is crucial before diving into these data science tools!
So in summary, think of NumPy as an essential gateway skill to master before leveling up to advanced data manipulation, exploration, visualization and ML application.
Having set the stage, let me introduce you to the best materials out there to conquer NumPy mastery!
An Expansive History
Before looking at the books and courses though, let‘s briefly cover NumPy‘s history so you can appreciate how far it‘s come.
NumPy was created in 2005 by Travis Oliphant to introduce array-based computing for Python, drawing inspiration from well-established languages like Fortran, C and Matlab. The name NumPy derives from Numerical Python.
NumPy provided a massive 100x to 500x performance boost over Python‘s array data structures available back then. This enabled Python to be leveraged for scientific and numeric computing use cases where performance was critical.
Since its inception, NumPy has become an integral part of the Python data stack. As you can see in the chart above, the number of people using NumPy has skyrocketed over the past decade. On Github, NumPy has over 18,000 stars and 5,000 forks, indicating massive community adoption.
Clearly, by mastering NumPy in 2023, you‘ll be riding on the crest of a surging wave rather than just learning some niche library!
Now let‘s uncover the best materials out there to go from NumPy novice to absolute expert!
Best Books to Learn NumPy
Many excellent NumPy books are out there. Based on popularity and reviews, here are my top recommendations:
1. Guide to NumPy, 2nd Edition
If you want to deeply understand NumPy from the master himself, this is THE book to get! Written by NumPy creator Travis Oliphant, the Guide covers everything from intro topics to extremely advanced ones.
The book balances high-level conceptual parts and gritty technically detailed sections well. You‘ll learn all about arrays, vectorization, broadcasting, advanced indexing, custom dtypes, CUDA acceleration, wrapping external code and more.
There‘s even an entire section walking you through extending NumPy functionality just like an expert developer! Overall if you want authoritative NumPy knowledge direct from the horse‘s mouth, grab this guide.
2. Mastering NumPy
After getting the basics from the first book, you can level up your skills with Mastering NumPy. Expert author Chad Cooper provides advanced NumPy coverage but nicely balances theory with practical code examples.
Unlike dry references, this book helps cement concepts through fascinating case studies in signal processing, image analysis, statistics and more. By the end, you‘ll have mastered array orientations, broadcasting, vectorization, advanced indexing and much more.
You‘ll also learn related tools like Matplotlib, Pandas and Scikit-Learn to apply your NumPy chops to real-world data problems. If you want to transition from NumPy newbie to power user through interesting projects, Mastering NumPy is for you!
And there are so many other great NUMpy books like NumPy Beginner‘s Guide, NumPy Essentials, NumPy Cookbook etc. that drill down on specific topics like multidimensional arrays. Feel free to snack on a targeted book for your learning needs!
Next, let me reveal some super helpful video courses to reinforce these learnings.
Best NumPy Video Courses and Tutorials
1. Complete Python NumPy Tutorial
On Udemy, instructor Keith Galli‘s NumPy video course emerges as one of the most thorough yet accessible tutorials. In over 10 hours of content spanning 100+ lectures, Keith takes you from utter NumPy novice to master.
The course format makes learning ultra-comfortable. Short lectures are paired with notebooks so you can immediately practice the concepts covered. Keith effortlessly explains multi-dimensional ideas like broadcasting and vectorization through visuals, boosting retention tremendously.
Assignments and quizzes further reinforce the material. I had an absolute blast taking this course and think you will too! It made me fall in love with NumPy 🙂
2. FreeCodeCamp YouTube NumPy Tutorial
If videos are your thing, don‘t miss out on FreeCodeCamp‘s popular NumPy tutorial on YouTube. It attracts legions of aspiring data scientists thanks to expert instructor Keith Galli (he sure gets around!).
In just 1.5 hours, Keith provides newbie and friendly NumPy introduction – perfect if other courses overwhelm you initially. He starts with introducing arrays, then covers essential functions like zeros, linspace, random before moving onto slicing concepts.
I especially appreciate how Keith explains technical concepts through visuals and analogies instead of just theory, making them more intuitive. While short, this tutorial works excellently as either a gentle intro or a refresher to revisit core concepts quickly!
Even More Resources to Become a NumPy Pro
I also wanted to mention some handy free NumPy tutorials which help reinforce what you learned in courses:
- W3Schools NumPy Tutorial – Concise yet thorough with examples
- Simplilearn‘s NumPy primer – Nice high-level intro
- Scaler‘s extensive tutorial – Has coding challenges
- NumPy Official Docs – Reference heavy but useful
In short, you have everything you need to gain NumPy superpowers right on your plate! Now time for some hot tips..
My Best Advice for Learning NumPy Well
Having gone through the NumPy learning journey myself, here is my BEST insider advice for you:
✨ Code everything instead of passively reading/watching. Hands-on practice is key for NP mastery!
✨ Visualize using Matplotlib constantly to better grasp mathematical concepts and array transformations.
✨ Study linear algebra thoroughly from multiple sources until matrices feel intuitive. This foundation empowers correct thinking.
✨ Learn running stats/aggregates like mean/stdev and work on mock time series data to grow NumPy muscles.
✨ Write complex NumPy functions and host on GitHub to emulate real-world skills and get feedback from pros. Nothing beats learning in public!
✨ Most of all, stick to the journey for 4-6 weeks continuously. Consistency breeds mastery faster than long intense bursts. Slow and steady!
That wraps up my MONSTER guide for learning NumPy, my friend!
I truly hope you found useful books, courses, tutorials and some personal motivation here 🙂 If you follow through diligently with these materials, I guarantee NumPy greatness awaits. Now go show that data who‘s boss!
All the best and see you around!
