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“I could always do data science if academia doesn’t work out.” It’s a recurring thought many graduate students and postdocs experience, especially if their work involves hearty servings of programming and statistics, the core elements of data science. Data science can be a rewarding alternative to academia, and academics do have many qualities that make them attractive candidates for data science roles. However, there are also often large holes in academics’ skill sets that can deter them from being hired straight off the bat.

So far, we’ve covered the technical side to data science: statistics, analytics, and software engineering. But no matter how talented you are at crunching numbers and writing code, your effectiveness as a data scientist is limited if you chase questions that don’t actually help your company, or you can’t get anyone to incorporate the results of your analyses. Similarly, how do you stay motivated and relevant in a field that’s constantly evolving?

Welcome to the fourth post in our series on how to enter data science! So far, we’ve covered the range of data science roles, some inferential statistics fundamentals, and manipulating and analyzing data. This post will focus on software engineering concepts that are essential for data science.

Welcome to the third post in our series on how to enter data science! The first post covered how to navigate the broad diversity of data science roles in the industry, and the second was a deep dive on (some!) statistics essential to being an effective data scientist. In this post, we’ll cover skills you’ll need when manipulating and analyzing data. Get ready for lots of syntax highlighting!

In the last post, we defined the key elements of data science as 1) deriving insights from data and 2) communicating those insights to others. Despite the huge diversity in how these elements are expressed in actual data scientist roles, there is a core skill set that will serve you well no matter where you go. The remaining posts in this series will define and explore these skills in detail.

The data science hype is real. Glassdoor labeled data scientist as the best job in America four years in a row, nudged out of the top spot only this year. Data science is transforming medicine, healthcare, finance, business, nonprofits, and government. MIT is spending a billion dollars on a college dedicated solely to AI. An entire education industry has sprouted to train new data scientists as fast as possible to fill the burgeoning demand, and for good reason: when 90 percent of the world’s data was generated in the last two years, we’re in dire need of people who understand how to find patterns in that pile of numbers.