Career Advice for Students from 2017 Data Science Leaders

As a student interested in data science, it’s not always straightforward to know exactly what you should focus on to get that first data scientist position. Also, being one of the faster-changing career paths, it’s not always clear when certain pieces of career advice have become a bit dated or are even applicable in 2017.

I reached out to several data science leaders of various backgrounds to get their thoughts on this; below are their responses to this question:1

What advice would you give to students that are interested in becoming data scientists?

Simon Petit, Cofounder at dataroots:

“To my opinion, students willing to become a data scientist or to work in this specific area should be naturally curious in life, eager to learn new things and not scared of thinking out of the box.”

“Secondly, they must develop a strong background in applied statistics and mathematics combined with programming skills to be at ease in the different techniques of analytics later on.”

“Third, they need to go beyond their expertise and be able to understand and adapt to any business they work for. Last element, communication and social skills are very important when talking to clients (business owners) and explaining the models and their benefits.”


Dan Valente, Head of Data Science at Knotch:

“Be curious, be skeptical, never stop asking questions (even when you think you have the answer!), learn as much math as you can, and get very comfortable programming.”


Note: Dr. Saigal is giving advice that is specific to high school students.1

Dr. Sanjay Saigal, Executive Director, Master of Science in Business Analytics at UC Davis:

“I tend to find that high school students don’t suffer from a lack of technical education as much as a lack of curiosity. That is to say, being creatures of culture (like all the rest of us) they don’t very often seek to cultivate the scientific temperament.”

“Were I advising high schoolers, I’d recommend that they look for opportunities to investigate truth – whether be it using methods of analytics or chemistry or any other science. Of course, learning statistics and computing helps discover truth too!”


Emily Glassberg Sands, Director of Data Science at Coursera:

“The technical skills are just one piece of the puzzle – necessary to be good but not sufficient to be great. Push beyond the technical. Think deeply about the product and business sides of the challenges you’re trying to solve.”

“Find a company where you care deeply about the end goal – the product fascinates you, the social mission speaks to you, whatever. You deserve to wake up every day excited about both the how and the why.”


Anahita Hassanzadeh, Data Science Manager at The Climate Corporation:

“Get your hands dirty with open-source data challenge questions such as the ones on Kaggle. This will help you gauge your strengths and weaknesses and also will make your resume stronger.”


Bill Vorhies, Editorial Director at DataScienceCentral:

“If you’re interested in becoming a data scientist you should look specifically for a college that has a data science curriculum, not just computer science.”

“Think also about taking business courses or getting specific business experience in the industry you’d be most interested in since data science is about solving business problems and creating business value, not about math or computers.”

“Mastery of predictive analytics will get you 8 out of 10 data science jobs but if you want to work at the cutting edge, prepare for a Masters that focuses on deep learning using neuromorphic or quantum techniques. Both those will be coming strongly on line over the next four years and will be in great demand.”


In Closing

Thanks to all our contributors for sharing their thoughts!



  1. Note: The contributors quoted for this article were asked slightly different subtypes of this question (ex: advice targeted to high school vs college students).  After going through the responses, I thought it’d make more sense to combine the responses and put them all in one article.  Any errors and omissions are my own.


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