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Summary:  Once you’ve decided to become a data scientist, where should you get your education?

This is a continuation of the ‘how to become a data scientist conversation’ (see “So You Want to be a Data Scientist” at  While most practitioners today have not come through formal data science education, it’s pretty much inked in that over the next three to five years having a ‘data science’ degree will make your entry into the field a lot easier.

It goes without saying that there is an explosion in degree and certificate granting courses occurring right now.  The first issue of identifying your options is what we’ll address here.  The second issue of evaluating the competencies offered in the curriculum and the quality of the education we’ll leave for another time.

This isn’t meant to be an exhaustive list but at least a convenient entry point to finding out what educational opportunities await.

In my previous article we gave this link to one of many fairly comprehensive lists of non-university programs that will train you to become a data scientist:  This time I’ll focus on college courses, most of which are degree granting with some certificate programs included in the lists.

The key phrase when you turn to Google is ‘Data Science’.  Turns out there are some courses to be found under ‘Predictive Analytics’ but DS returns a much richer field.

One of the most complete lists I’ve come across is “Colleges with Data Science Degrees” by Ryan Swanstrom which you can find here  The list derived in 2012 includes 469 colleges.  Most are in the US but a few overseas.  What’s fun about this list is that Ryan used an R program to scrape the information from the web.  Here are some interesting stats from his list:

By type:

                Bachelors            25

                Certificate           84

                Doctoral               17

                Masters               343

My read on this is that the Masters programs are the core of where data science education is these days and that the certificate programs are right behind.  I think the Doctoral program count is very low as is the undergraduate count.

The updated 2014 list can be seen here: but has only 279 colleges.  Given the excitement around data science and the profit potential for colleges it’s reasonable to assume that 40% of colleges didn’t stop offering the degree in the last two years.  Probably just the opposite and that’s the reason I’ve left the older list here as an important resource.

I expect there are a lot more undergraduate majors out there that feature data science but probably carry degree names like computer science or engineering and didn’t get picked up by Ryan’s program.  Which is to say, if you’re just starting your undergrad years you should be able to find a data science program that will work and that a Masters is not a requirement.

The titles of these programs are all over the place and this is just a random sample.  Most contain the key words Data Science or Analytics. 

  • Data Science
  • Business Data Analytics
  • Business Analytics
  • Data Analytics
  • Computational Data Sciences
  • Computational Modeling and Data Analytics
  • Data Analysis and Management
  • Information Systems and Decision Sciences
  • Applied Statistics
  • MBA - Business Intelligence and Information Technology focus

Although there are very few MBA programs on this list, if an MBA is what you want you should look into MBAs with a data science focus.  I would think that at least one-half the course work should be data science specific if you want to be competitive with other DS Masters graduates otherwise you’re just getting an introduction and that’s not likely to make you competitive for data science jobs.

Here are some other sources for you to check out:

This is an interactive map with details of college DS programs.  Can’t tell how many are actually mapped but it looks like 50 to 100.

23 schools with Masters in data science:   

11 Universities.  About half are Masters programs.  

Big Data analytics top 20 programs:

Top Predictive Analytics programs: 

On-line and part time data analytics programs:

A little about the quality and content issue. 

  1. Not all programs in Data Science are of equal quality.  Be sure to study and compare the course offerings carefully.
  2. Look back at the ‘So You Want to be a Data Scientist’ article and focus on the difference between Big-Web-User DS versus Core DS.  If you want to do Big-Web-User DS which are advanced ML techniques on NoSQL (Big Data) then the curriculum had better spell out that it includes data blending/data lakes, recommenders, IoT, natural language processing, and image processing.  However, if you are headed for a Core DS career most of the education should be focused on predictive modeling with some exposure to these other topics. 

This is probably the last time anyone needs to write this type of article (though I suspect it won’t be).  There are now so many offerings and new ones are coming on so fast that in a year or two there probably won’t be any self-respecting colleges that don’t offer a data science degree. 

Although these listings are heavy on Masters programs, those of you still on the under graduate path should find plenty to pick from.  The big predictive analytic platform providers SAS and SPSS have made their mark primarily by targeting undergraduate programs.  You may just have to dig a little deeper than the degree titles to ensure the course work covers all the data science bases.

If you are motivated by Data Science then I’m sure you’ve already realized that this is a new and fast evolving domain.  Beyond your education you’ll need to be a life-long-learner to keep up and help us all expand these capabilities.


About the author:  Bill Vorhies is President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001.  Bill is also Editorial Director for Data Science Central.  He can be reached at:

[email protected] or [email protected]




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Comment by C. Sachs on November 7, 2015 at 7:32am

The problem I have with the concept of Data Science degrees is that they may tend to be too heavily focused on technical aspects of the work. I've found that context is of equal importance in the job I do. So, if one wanted to be a Data Scientist involved, say, in Public Health, it would behoove one to get substantial coursework in Public Health along with the technical coursework in analytics, statistics, data modeling, programming,  etc., for Data Science. Likewise, if one wanted to be involved in Business, then business and economics courses would be good to take along with those technical courses.

It just seems to me that Data Science is an interdisciplinary degree. While one could learn the technical skills, they really don't do a lot of good immediately without learning a knowledge domain context in which to apply them. Sure, one can learn such context "on the job," but that could take anywhere up to a year or more to get the appropriate context -- and there will always be some "on the job" learning that will take place anyway as any business operation will have their own business processes and data bases/structures that have to be learned and, if not well documents, figured out.

Comment by Sione Palu on July 23, 2015 at 3:48pm

Another add-on to my previous comment.  The PhD intern recruited at the beginning of the year for 6 months (who has become full-time staff now in the Data Science team) had no knowledge of machine learning at all. Also little statistics knowledge as his area he did his PhD thesis was on partial differential equations applying to option pricing & financial markets. I requested him to send his thesis. His lack of machine learning & statistic's knowledge swayed some team members from him, but I put more weight in his favour after reading his thesis. Anyone who understands partial differential equations can also self taught to understand machine learning & that's fact. Now this team member has absorbed machine learning techniques very fast. No one has taught him. All we do is to point him out resources (open sources & academic papers) to self study. This is what we are looking for. Those who can lift themselves up with minimal help. We focus less on those who have been doing data analytics for years but hasn't been exposed to academic type research which is what we do. We still use some open sources but we also dig into academic literature because there are new techniques that just become available in recent years that we wanted to explore. This is where we see doctorates & masters candidates can rise up. Last year we recruited a physicist who she did her intern at CERN. This physicist is a star in the team. She herself also came with no background in machine learning.

Companies may get caught in hype of data science which blinds them to the forest. They mistaken the trees for the forest in just checking for term "Data Science" in a CV or a qualification to see if that candidate can proceed to the next stage.

Comment by Sione Palu on July 23, 2015 at 2:06pm

Quote : "While most practitioners today have not come through formal data science education"

There is not widely adopted formal education call data science. The term just sprung into existence about 3 years or so ago. And how can we look for data science education candidates when those data science courses just started 2 years ago? It means that they haven't graduated yet, perhaps in a 3 year course.

We haven't hired anyone with a data science qualification. The last 2 PhD interns we hired were, one is a mathematician & the other one is a computer scientist.

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