However, in this article, I want to highlight the importance of learning the basics first. You cannot learn TensorFlow before you’ve learned Python. Similarly, you cannot master data science in Python without knowing NumPy.
NumPy is at the heart of both data science and machine learning. Scikit-learn uses NumPy arrays. Pandas builds upon NumPy. TensorFlow uses NumPy operations not only for preprocessing. Matplotlib uses NumPy to create histograms, plot and shape data. No matter where you go in the data processing space, NumPy is already there.
With the growing importance of Python, it becomes more and more important for you to know NumPy. If you struggle with NumPy, then check out my book “Coffee Break NumPy” (link to my book page). It’s a fun way of improving your NumPy skills by solving rated Python puzzles.
So why should you learn NumPy? Because the most important major programming language for machine learning and data science heavily builds on NumPy. By mastering NumPy, everything becomes way easier in these hot new areas.
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.