The Data Science Handbook, 2nd Edition

Interlude: My Personal Toolkit

Every data scientist has their own set of preferred programming languages, libraries, and other tools. You will have to decide what works best for you. To give you a data point though, here is how I work when do data analysis:

• My main programming language for data science is Python. I know it, I love it, and I can do just about anything with it. I also use it for production coding whenever I am choosing the tools and there’s no good reason not to.
• I use Pandas as my main data analysis library, and I supplement it with scikit‐learn for machine learning.
• I usually use matplotlib for visualizations, but I am aware they’re not as pretty as I would like.
• My actual coding is usually in a Jupyter notebook, rather than an IDE.
• When I’m contributing to a large production codebase, I usually use Visual Studio. But I’m pretty open about IDEs and text editors, and I’ll admit I’m not an IDE power user.
• I do most of my work on a Mac, but that’s just because it’s what my employers tend to use. I usually do hobby projects using Linux, and I’m hoping to do more work on Windows in the future because they have a famously great set of tools for developers.
• When I’m doing Big Data I use PySpark, which I’ll talk about in the chapter on Big Data.
• For deep learning, I have historically used Tensorflow, but am switching gears toward PyTorch because a lot of the cool libraries are written using it.

I used to use R, but not anymore if I can avoid it. The syntax has always ...