In this four part series on how to learn data science, I previously outlined the dimensions of data science and provided resources for learning data science. However, as previously mentioned, the best way to learn data science is to work through a whole problem from beginning to end.  

In a previous post I outlined the dimensions of data science and provided a checklist for the skills needed in each of the four areas. In order to help you navigate these dimensions and create your own learning journey, I’ve compiled a list of my favorite books, articles, and classes for each of these areas along with some general tool recommendations.

One of the most common questions I get asked is, “what skills do I need to be a data scientist?”. To help with this question I wanted level set on what I believe are the core competencies of data science and break down these dimensions into a format that could be used as a checklist to self evaluate yourself for your learning journey.

One of the first things any good data scientist does before building a predictive model is to explore their data. The temptation can be to rush through this exploration stage in order to get to a working product faster.  However, if we take just a little more time in this area we can save hours of work, build better models and troubleshoot issues much faster. To get you started, I’ve outlined a few principles for data exploration.