Just starting out? Let me help you figure out what to learn and how to learn it.
When you think of “data science” and “machine learning”, do the two terms blur together, like Currier and Ives or Sturm and Drang? This article I wrote for KD Nuggets will clarify some important and often-overlooked distinctions between the two to help you better focus your learning and hiring.
Learn Key Differences Between ML and DS
The two most popular open source languages for data science are R, which is favored by scientists, and Python, which is favored by engineers. Which choice is right for you in your situation and with your requirements? Find out in this article.
Choose R or Python for Data Science
During my years of teaching data science to working software engineers, I've learned what can be cut and what is required. I'm making my full curriculum available — I wish I'd had this when I was starting out! Topics covered are exploratory data analysis, statistics, and predictive modeling.
I'm currently developing a Python curriculum, using pandas, altair, and statsmodels. It's not quite done yet, but I've put it up so my students can refer to it. While developing it, I also reviewed several Python visualization libraries.
Learn data science using Python
Finished my introductory sequence, or already have experience in the field? Check out these more advanced topics.
My list of advanced reading is organized by topic. You can improve your skills in these areas:
I can't recommend every book I've ever read. I'm keeping my comprehensive list of reviews, including the ones I didn't like and the ones somewhere in between, on Goodreads, an external site.