Dueling Schooling On Big Data And Analytics

There’s been a lot of discussion about the shortage of quantitative analysts and data scientists in this world, and many people wonder where they will all come from. Today I have good news and bad news for you. The good news is that there are a rapidly growing number of educational institutions that are offering courses, concentrations, and degree programs in analytics and big data.

A 2012 survey of “The State of Business Intelligence in Academia,” for example, found that there were 70 degree programs and 130 degree concentrations in business intelligence and analytics—and the survey primarily involved business schools in the US. The total number of programs is probably substantially larger.

So your shortage of quants is likely to be remedied soon. What’s the bad news? Well, the graduates of these programs may not be quite as well-rounded as one might hope. Most of the programs take place within a single school at a university and offer a narrow perspective on analytics and big data.

Take, for example, the University of California at Berkeley—traditionally a hotbed of IT-related education. The university’s innovative School of Information recently launched an online Master of Information and Data Science program. It sounds like a great educational offering, but how does it compare to the Master of Engineering in Data Science and Systems, offered by the engineering school? Or the new programs and workshops offered on “Big Data Boot Camp” or “Theoretical Foundations of Big Data Analysis” offered by the Simons Institute at Berkeley, recently funded by a big gift from quant hedge fund honcho Jim Simons? There are undoubtedly valid differences between these programs (I think the Simons programs are mostly for faculty researchers, for example), but it may be hard for potential students to discern them. And wouldn’t it be nice if their content were somehow combined?

Berkeley is hardly the only school with overlapping programs. Northwestern has an online Masters in Predictive Analytics, and a full-time, face-to-face Masters of Science in Analytics. I pity the poor student who has to decide between “analytics” and “predictive analytics.” NYU offers a Master of Science in Analytics (from the Stern business school) and a Master of Science in Data Science (from the Graduate School of Arts and Sciences). One bright spot is that there is a school-wide initiative at NYU in data science and statistics, so at least the academics are talking to each other.

But why can’t we all get along? The earliest graduate degree in analytics, the Master of Science in Analytics at North Carolina State, was a cross-university initiative from its beginning in 2007. It combines course materials in applied math, statistics, computer science, and business. Funded by SAS (which itself came out of an NC State research program in the early 1970s), the integrated focus was a condition of the program’s funding. It’s not easy to get academics to work together across schools, but in NC State’s case it seems to have been worth the trouble. The program is fantastically successful in terms of student applications and corporate hiring of graduates.

Universities are not the only parties who need to cooperate more when it comes to advancing knowledge in analytics and big data. Professional associations also have some coordinating to do. Analytics and big data are important topics in several different professions: operations researchers (an optimization-oriented discipline), represented by INFORMS; statisticians, represented by the American Statistical Association and the Royal Statistical Society in the UK, computer scientists, represented by the IEEE; and actuaries, represented by the Society of Actuaries.

Some of these groups have been quite progressive in embracing the broader world of analytics. INFORMS, for example, under the visionary leadership of current president Anne Robinson, developed a “Certified Analytics Professional” certification program, and renamed its practice-oriented annual conference to the Conference on Business Analytics (and Operations Research). The actuaries, led by Business Analytics team head Lisa Tourville, have developed a set of recommendations for how actuaries can be broadly trained and encouraged to play in broader analytics fields.

The statisticians and computer scientists are relatively late adopters, however. Some statisticians harumpf that data science would be nothing without statistics, but don’t want to “lose their souls” by getting involved in the popular movement. Computer scientists in the IEEE only began to address big data in the last year or so, but are finally jumping on the bandwagon with a Big Data conference.

None of these groups talk much to each other. Perhaps there is a need for a new professional association around big data and analytics, in which all the component disciplines would be combined. It could be the association equivalent of the NC State Masters program. If anyone wants to supply a big pot of dough to finance such an group, please let me know!