As evidence of this, in a period of economic slowdown, where we read that 50 percent of college graduates can't get a job, college graduates with degrees remotely aligned with applied analytics have multiple offers in advance of graduation. Academic training in applied (versus theoretical) statistics is helpful -- and mitigates some of this talent gap at the entry level. Nonetheless, we all know it's insufficient to meet the growing demand for what we now know as the data scientist.
A scan of offerings at universities across the country shows that none will result in a single degree program in data science. As a professor, I can see at least three reasons why this is so:
- Universities historically are not massive ivory towers, but groups of towers called colleges or departments. Cross-discipline degrees aren't a strength of university curricula. Pity the engineering student who wants to minor in history or the student who wants to cobble together a degree in public policy of architecture and ecological science. Ask any student who has tried to cross colleges within a university to create a targeted degree -- it is the worst of bureaucracy, outdated registration systems, and academic-elite egos all wrapped in a Gordian knot. And yet this is exactly what data science is -- the intersection of mathematics, statistics, and computer science, combined with some potential area of content application like finance, biology, or sociology.
- The data tsunami washing over all companies, not just data-driven ones, is a fairly recent phenomenon. Professors who teach statistics and computer science, in particular, are recognizing that the traditional skills we have comfortably taught for years or even decades don't work in this environment. Concepts like p-values to derive significance are meaningless when you have a billion rows of data. Professors are being challenged to teach skills that many of them don't have. Some are rising to the challenge, and some (think tweed jackets with leather elbow patches) are just hoping it all goes away, which, of course, it won't.
- We need your data. Remember the datasets you saw in the classroom? They had 100 observations, three variables, and no missing values. Everything was significant in its raw form. Welcome to textbook data. We do our students an immense disservice by using this kind of dataset to teach analytics. But, believe it or not, in a sea of data, we are dying of thirst. Universities need massive, complex, unstructured, messy data with missing and (mis)coded values for use in the classroom. Ultimately, we can't teach data science skills without big-data.
I encourage people within the public and private sectors to partner with universities and in particular with professors who have recognized these issues and are trying to pivot their curricula to meet the needs of the marketplace. Sit on advisory boards. Provide real datasets (scrubbed as needed). Offer to speak in the classroom of your experiences with big-data -- everyone's story is the same, but different. Partnerships with universities in this area are particularly important and mutually beneficial. You can help us train your future data scientists.
Do you agree that change is needed if universities are to educate the data scientists that businesses increasingly need? Share your thoughts below.