An interesting article about the increasing demand for data scientists and the centrality which corporate imperatives should have on data science syllabi. How will data science change as it scales? It seems inevitable that undergraduate programmes will play a crucial role in meeting this demand:
o what’s the problem? We have a global talent shortage, and the demand for data scientists continues to grow rapidly, far outpacing the anemic growth in supply. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.
The need for experts trained in extracting insights from data is more important than ever.
The global talent drought may be partially caused by the dearth of universities that offer data science programs dedicated to preparing the next generation of data scientists. Fewer than one-third of U.S. News & World Report’s Top 100 Global Universities offer degrees in data science. Of the 29 universities that offer data science programs, a mere six make them available to undergraduates (the rest are reserved for graduate students).
Moreover, the average cohort size for one of these data science programs is just 23 students. The small cohort size may be partially attributed to the fact that most of these data science programs tend to be offered to graduate students, and sizes of graduate programs tend to be smaller. Still, at 23 students per cohort, we are unlikely to make a meaningful dent in closing the global data science talent gap.
An analysis of the curricula for these data science programs also suggests cause for concern. Most are focused almost exclusively on the computer engineering aspects of data science, with course titles such as Software Design, Parallel Computing and Software Development.
Largely missing from the data science curricula at many of these universities are courses in statistical analysis, insights and strategy. This oversight may have serious consequences for graduating students and their future employers.
Without training in these other areas, data scientists may be capable of designing an algorithm that is mathematically elegant, but doesn’t make strategic sense for the business. They also may not know how to design an experiment to determine whether the algorithm is effective. In other words, computer science skills alone are insufficient for success as a data scientist in today’s marketplace.
Furthermore, many data science programs lack courses that help students apply their technical data science skills to fields such as marketing, operations, product development and supply chain, and industries such as energy, bioinformatics, transportation and healthcare.