Tracking down answers to your questions about data scientists

Do you need a data scientist? Want to be one or improve the skills you already have? Check out our Insights series.

By Stephanie Robertson, SAS Insights Editor

Data scientists have been in the headlines ("Sexiest Job of the 21st Century") and compared to unicorns because of their rarity or suppositional nonexistence. So it's no surprise that they're highly courted and well-paid by many organizations around the world.

One day in late 2014, the Kaggle job board showed numerous postings for data scientists. From Allstate Insurance to Capital One. From the developer of an autonomous driving vehicle to an international art market organization – it seems like almost everyone is looking for a data scientist.

What is Kaggle?

Kaggle was created in 2010 and is the world's largest community of data scientists. From this site, they compete with each other to solve complex machine-learning problems, and the top competitors are invited to work on problems from some of the world’s biggest companies through Masters competitions with big money payoffs. There's also a job board and plenty of learning opportunities. Find out more about Kaggle.

  • L’Oréal wanted to anticipate consumers’ needs.
  • The Philadelphia 76ers needed to forecast plays, players, teams and seasons.
  • The Weather Channel was looking to optimize online ad placement.
  • For publisher Simon & Schuster, it was optimizing e-book prices.
  • A residential cleaning service in San Francisco wanted to find which scents work best for homes.
  • An internet radio start-up had tons of data and ideas but couldn't provide relevant playlists.

Several themes seemed common: the ability to draw insights from big data (Hadoop was often mentioned), familiarity with a range of programming languages and good communication skills. We begin to wonder:

  • How do organizations know if they need or are ready for a data scientist? What can they help you do?
  • Who are real-life data scientists? What do they do? Did they always yearn to be one and how did they get there?
  • And finally, if you want to become a data scientist, what should you do?

Ready for a data scientist? Get advice from the experts.

For an introduction to this topic, we turned to Dr. Michael Rappa, founder of the Institute of Advanced Analytics at North Carolina State University. This was the first university in the US to offer a Master of Science in Analytics, and it has evolved to offer specific training for students interested in becoming data scientists. (There are now there are more than 60 other similar programs in the US, as well as others around the world.) Rappa gave us his take on the increasing number of data scientists’ jobs – he sees it as a trend that will continue. He also shares his opinion on what makes data scientists different from other types of analysts, statisticians and data miners. He provides advice for those looking to hire data scientists, and he has lots of ideas for those seeking a successful career path in that field.

Read our interview with Dr. Michael Rappa from NCSU

Suggestions from MIT Sloan on how to get the most value from your data scientists

NEW! Tips for executing a successful data science strategy from TDWI

Real-life data scientists – yes they do exist!

After that, we wanted to talk to some data scientists ourselves. We asked around to see if anyone knew any who would agree to interviews about their jobs and training. And we can say – without doubt – data scientists do exist because we interviewed almost a dozen from around the world. We’ll be posting new Q&As frequently. We asked the same questions. See if you think there are familiar threads running through those conversations.

Meet the data scientist: Manuel-David Garcia

Meet the data scientist: Alex Herrington

Meet the data scientist: Kristin Carney

The thrill of problem-solving. The lure of large paychecks. How to become a data scientist.

By now, you may be thinking, “Wow, that sounds pretty cool. I wonder if I’ve got what it takes to be a data scientist. How would I get started?” In this section, we’ll add articles with advice for those who want to rule the world of big data. As mentioned above, many colleges and universities now offer degrees in data science as well as advanced training in analytical methods ranging from statistics, machine learning and data mining to predictive modeling and wrangling huge amounts of data. There are online options as well.

And, because SAS is committed to helping our customers get what they need, last year we announced SAS Analytics U. It's a comprehensive global program that offers professors, students, academic researchers and independent learners access to free SAS software, helpful resources, free online classes and an interactive, online SAS Analytics U Community.

Learn more about the analytics skills gap and SAS Analytics U in this blog post.

For fun, take this short online quiz from NCSU to see if you're analytically ready.

Master SAS skills with courses from our data scientist learning path.

And the data scientist said...

Now that we've had this series running a few weeks, we're starting to hear from some data scientists on various topics. We'll publish those stories here.

Keeping the science in data science by Patrick Hall

Taking a team approach to data science by Jennifer Nenadic

Check back often!

Once we got started, the stories poured forth. It’s definitely a hot topic and there’s a lot of information out there. We hope you’ll check back often to learn more about the no longer mysterious, but infinitely interesting, data scientist.

From the editor: I'd like to thank my Analytics Insights team – Malene Haxholdt, Nancy Rudolph and Katie Whitson – for their help putting this series together, and also my fellow Insights editors Daniel Teachey, Anne-Lindsay Beale and Jeff Alford for their ideas and support.

Data Scientist Series

Read More

Get More Insights


Want more Insights from SAS? Subscribe to our Insights newsletter. Or check back often to get more insights on the topics you care about, including analytics, big data, data management, marketing, and risk & fraud.

Back to Top