Resources for Learning Data Science

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.

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Sit Down, Be Humble

Working in the world of data, humility is usually the last thing I think about. I’m not a proud person by normal standards, you won’t find me shouting in meetings when I don’t get my way, or telling people how much more I know than them. However, it’s recently come to my attention that I need a dose of humility,  especially in my work with data.

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Keys to Data Exploration

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.

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