Best Data Science Career Guide

In May 2023, my contract writing employers began including an increasing number of data science assignments in my monthly quota. Although data science was already in its ascendency when I retired from full time work in 2019, I had only written articles about a few research papers published by Rice University computer scientists. The current trajectory of data science as a career was something I struggled to grasp. But no longer.

Read Build a Career in Data Science

In July, I began reading the book, “Build a Career in Data Science,” by Rice alumna Emily Robinson, now a senior data scientist at Game Data Pros, and Jacqueline Nolis, a principal data scientist at Fanatics, Inc. Having read their stories in the book and listened to hours of their podcast, it feels like Emily and Jacqueline are now included among my closest mentors and I can’t write this fan tribute to their work by referring to them as Robinson and Nolis (which is the best journalistic practice). So, meet my new career crushes, Emily and Jacqueline, and begin to breathe in their practical advice for new data scientists.

They were awesome data scientists before they wrote a book about it

Emily and Jacqueline had spoken at numerous conferences, contributed to the data science community through social media and blog posts, and were working as data scientists in different companies when they decided to collaborate on a book. Their guide, “Build a Career in Data Science,” is full of informative stories and advice for each step of a data science career, from how to acquire training to leaving a job with good grace. For someone who needs to understand data science and what the industry’s roles and responsibilities include, it is a gold mine. For someone who wants to work as a data scientist, it is a lifelong reference manual.

Jacqueline’s background includes a Ph.D. in industrial engineering, and Emily’s includes a Master of Science degree in management and organizational behavior. Emily then enrolled in a data science bootcamp while Jacqueline was discovering what she liked most about her engineering roles was the focus on business and data analysis. Their varied careers mean they know a lot of data scientists in a wide range of companies and industries. And that is how they were able to include personal stories far beyond their own experiences in each chapter of this book. We’re talking detailed insights from data scientists at startups, FAANG companies, brand faves like Airbnb and Spotify, giants like Boeing and IBM, and every size organization in between.

Read about each step of a data science career, and listen to additional insights

For pleasure, I read about a book a day but for a month, “Build a Career in Data Science” became my choice for my down time reading. It is an easy read; I chose to take my time with one chapter each day and mark up the explanations that I knew I would refer to for future articles. Then I listened to their podcast of the same title. If I knew in July what I know now, I would slow down my reading even further in order to listen to the podcast at least at the end of each chapter.

The podcast has many MORE insights about data science and the people who build their careers on its foundations --including the hilarious ‘sponsor’ commercials Jacqueline created. There are also several bonus episodes, including what is perhaps my favorite – the final final bonus episode when both Emily and Jacqueline were out of work at the same time. As much as I loved the podcast, the book itself remains my go-to resource for its bullet lists, sample questions, references to additional books and websites, and other study guide-style details that don’t translate well into a podcast.

Give the book to Granny

Talking to family and friends about a data science job is tough. Data scientists have as many specialties as doctors and lawyers, but perhaps one day data science careers will be so familiar that instead of asking, ‘What’s that?’ your family and friends will ask, ‘Oh, what area?’

This holiday season, rather than dreading the coming questions about your job, hand “Build a Career in Data Science” to Granny when you greet her with a hug. Identify page 3 with fun bookmark and ask Granny to read to page 5. She will be hooked and probably burn the turkey, roast, or ham while she continues turning pages. By the time Aunt Marge comes into investigate the burning odor, Uncle Joe and will be reading over Granny’s shoulder and two cousins will be on their phones looking for data science majors and bootcamps.

By the time your family and friends sit down to the meal, they will be looking at the food as a data science project. How many desserts v entrees or vegetables are being served in this neighborhood? What do grocers need to know to about today’s food choices to prepare adequate ingredient options for the next holiday feast? This year’s flavor trend seems to be pumpkin but we’ve also seen cranberry, pomegranate, and butternut squash trending over the last decade; how do we predict the next flavor trend?

Buy it for yourself

As much fun as you’ll have discussing your data science career over holiday gatherings, you’ll enjoy savoring this book even more alone. Buy this book if you have any interest in learning about data science careers or possibly shifting to more analysis work in your current role. Buy this book if you are already a data scientist. If you are still in the early years of your career, ?jump to Chapter 11 or 12 – Deploying a mode into production and Working with stakeholders.

If you are wondering how much longer you need to remain in your current data science role, jump to Chapter 15 (Leaving your job gracefully) or Chapter 16 (Moving up the ladder). And if you are one of those people who always skip to the end before really settling into a book, jump to page 275 and read the interview with Angela Bassa, then head of data science for iRobot and currently working for Apple in infrastructure executive decision analysis. She talks about leading data scientists and engineers, the option of working as a contractor, and the top two things junior data scientists need to practice to best grow their careers.