Learning Wolfram: From Zero to Hero
Jumpstart Your Data Science Chops With This Quick Introduction
The Wolfram Language provides a super-rich notebook interface and thousands of well-designed functions to make data science projects a delight. Getting started with the Wolfram Language is now easier than ever with great tutorials, exhaustive reference documentation, and context-sensitive help.
As a long-time programmer in the Wolfram Language, I thought it would be useful to share my view on how you can add this powerful application to your data science skillset.
The best place to get started is the “Fast Introduction for Programmers” tutorial. It gives an excellent introduction from the basics of defining functions to more advanced topics like building interfaces and deploying notebooks, graphics, and images to the Wolfram Cloud. Each section also comes with a video that covers the same topic, which gives you another great option to learn.
You can immediately start using the Wolfram Language by clicking the “Get Started” buttons in the tutorial. This opens a notebook in the Wolfram Cloud without the need to download or register for anything. Just try the examples from the tutorial and start experimenting!
The next step to hone your programming skills is to visit the “Wolfram U” learning website.
For data scientists, there are some really really nice courses and videos in the section for “Data Science & Statistics”. The main course here “Multiparadigm Data Science is an online course with narrated videos, notebooks with code that can be used in a scratch coding area:
Every section comes with a quiz so you can test yourself easily.
At this point, I would recommend signing up for a free, basic Wolfram Cloud. This gives you the ability to go wild with programming experiments while you’re still learning the language:
Another option is downloading the “Free Wolfram Engine for Developers”. This gives you access to the core language on your local machine. It does not include the nice Wolfram notebook interface, but you can hook this engine up to the Jupyter notebook interface.
Additionally, many people have developed plugins for various IDEs which are all listed on the main resource page for developers:
If you want to experience the full Wolfram notebook interface (and I highly recommend this!) you can start with a trial of the Wolfram Desktop that is available here:
Finally, the best long-term resources for being productive in the Wolfram Language are the extensive reference documentation, which is deeply integrated into notebooks. There are at least two great user communities where you can ask questions. First, the Wolfram Community, which is a great place to not just ask questions but also share your work with others. Second, the Mathematica & Wolfram Language Stack Exchange site is great for asking (and answering) questions.
I hope this was useful to you and I wish you good luck with your next data science project!
