Data Science: The Hard Parts

Preface

I’ll posit that learning and practicing data science is hard. It is hard because you are expected to be a great programmer who not only knows the intricacies of data structures and their computational complexity but is also well versed in Python and SQL. Statistics and the latest machine learning predictive techniques ought to be a second language to you, and naturally you need to be able to apply all of these to solve actual business problems that may arise. But the job is also hard because you have to be a great communicator who tells compelling stories to nontechnical stakeholders who may not be used to making decisions in a data-driven way.

So let’s be honest: it’s almost self-evident that the theory and practice of data science is hard. And any book that aims at covering the hard parts of data science is either encyclopedic and exhaustive, or must go through a preselection process that filters out some topics.

I must acknowledge at the outset that this is a selection of topics that I consider the hard parts to learn in data science, and that this label is subjective by nature. To make it less so, I’ll pose that it’s not that they’re harder to learn because of their complexity, but rather that at this point in time, the profession has put a low enough weight on these as entry topics to have a career in data science. So in practice, they are harder to learn because it’s hard to find material on them.

The data science curriculum usually emphasizes learning programming ...