Python programming is a critical skill for data engineers. When it comes to working with data, there’s a powerful library that can increase your code’s efficiency dramatically, especially when you’re working with large datasets: NumPy.
At present, this is the ninth of 14 courses in our Data Engineering path — we recently added a course on Algorithm Complexity as well.
Completing the Data Engineering path requires a Premium subscription, but you can try out the first mission of this new course, or any other course in the path, with a free account — no credit card required!
What will I learn in this course?
This course offers a start-from-scratch education on NumPy for data engineers. That means you won’t need to have any prior experience with NumPy, and you won’t be wasting any time learning things that aren’t relevant to data engineering work.
After taking a tour of the basics, you’ll quickly start using NumPy to build and manipulate two-dimensional and three-dimensional arrays. Mastering arrays will allow you to perform calculations across large swaths of data at once, rather than looping through it row by row, saving you time and processing power.
As the course gets into more advanced applications of NumPy, you’ll also learn how to assess your memory usage, and you’ll learn about the limitations of NumPy. This provides a great lead-in to the next course in our Data Engineer path: Processing Large Datasets in Pandas.
By the end of these two courses, you’ll be able to use your Python skills and your new NumPy and Pandas knowledge to work with and process huge datasets much, much more efficiently than is possible with stock Python.
And of course, you’ll be doing all of this in our interactive, in-your-browser platform. You’ll work with real data and write and run real code without having to worry about downloading datasets, installing libraries, or any other hassles.
Why do data engineers need to learn NumPy?
NumPy is one of the most popular — and powerful — libraries for data work in Python. In fact, it’s so powerful that pandas, the most popular Python data science library, depends upon and makes use of some NumPy functionality.
From the perspective of a data engineer, NumPy’s chief advantage is that it allows you to do vectorized math using arrays. This approach is far more efficient than looping through each row of a dataset one at a time to perform calculations.
The efficiency that the array operations in NumPy offer when compared to “stock” Python is particularly critical for data engineers, who’re often dealing with huge amounts of data and tasked with processing it as quickly as possible.
Ready to start learning NumPy? Dive in and try out the new course for free (no credit card required!)
Or, start from the beginning with our Data Engineering career path (try free, zero prior experience required).