As you know, data science is super hot.  Whether you're analyzing data,  working as an engineer, or creating fancy machine-learning models, your job likely involves all sorts of mathematical operations.  

This sort of work requires fast, efficient processing. And thus, for years, people used the fastest-executing programming languages they could find — C, C++, and Java.  In many cases, they used specialized languages and tools, such as Matlab, which did the job but cost quite a lot.

In the last few years, Python has become the #1 language in data science.  This puzzles a lot of people, since Python is known for being friendly, but not for being efficient.

The key to this change is NumPy.  NumPy is a library that puts a thin Python shell over C arrays.  You thus get the ease of use of Python, but the efficiency (in both space and time) of C.  

Not surprisingly, NumPy is wildly popular.  But learning to use it can take a bit of time, because it works so differently from built-in Python data structures.  Even if you're a master at Python's lists, tuples, and dictionaries, NumPy requires that you think in different ways. 

For example, Python lists can contain any type of data.  But NumPy arrays, because they're built on top of C, have a "dtype" associated with the entire array, describing what type of data they can (and cannot) hold.  Learning what types are available, and how to choose from among them, can take some time.

I've been teaching NumPy to companies around the world for close to a decade, and I've been teaching Python for even longer than that.  This course is a version of what I teach in to engineers in some of the world's most famous companies — but available to anyone with an Internet connection, and a desire to learn.

In 75 lessons and 60 exercises, I take you through each part of NumPy that you need in order to use it effectively.  I show you not just how to use NumPy, but why to use it, and in what ways you can use it more effectively.

After you take this course, you'll have a powerful new tool in your toolbox.  You'll be ready to take on data-analysis challenges in your current job, as well as apply for more challenging jobs down the line.

I'm confident that you'll get a ton out of this NumPy course.  If you don't, then just e-mail me, and I'll refund your money.  No time limit, either — I trust that you're reasonable.