What is the who, where, and when of Data Science education?
Ever since the Harvard Business Review declared five years ago that Data Science was the “sexiest job on Earth”, students have flocked by the tens of thousands in pursuit of degrees in the field.[i]
Earlier this year Alphabet’s Eric Schmidt stoked the flame further in declaring, “a basic understanding of data analytics is incredibly important for this next generation of young people. That’s the world you’re going into.”[ii] Elon Musk’s worries over the lack of data specialists has pushed him to finance Neuralink, a company whose mission is to physically link computers to the human brain to improve the speed of data processing. LinkedIn reported not less than 14 300 openings for data scientists this week, with over 774 opportunities in France alone. Amid such a frenzy, what is the who, where, and when of Data Science education?
What is Data Science?
The basic question of what is data science is as relevant today as it was in the beginning of the century when William S. Cleveland suggested this new academic discipline extended the field of statistics to incorporate “advances in computing with data”.[iii] Today, organizations are looking for data scientists in industries ranging from biotechnology to finance; IT and services, and marketing to public service. The scarcity of specialists to fill these openings is largely due to the fact that Data Science is marketed as a mashup of analytical, business, and technical skills that are rarely found in any one profile. In reviewing the current job openings, data science appears to be a confusing mix of management consultancy, computer systems analysis, information security, operations research, and statistics. The only common denominator is a universal vocation of using data to learn about real life business challenges.
What then is the essence of a data science education? This focus on business problem solving separates Data Science from computer science and statistics. The fact that consumers, managers, and stakeholders not only take decisions, but interpret the data differently strongly suggests that data science cannot be reduced to algorithms and machine learning. Ed Chen points out the paradox between the number of schools offering to teach “data science” and his personal conviction that the lessons of data science cannot be learned in school.[iv] A data science education necessitates both theory and practice — theory to understand the nature of the problems that business faces today, and practice in understanding how to transform the date into decisive action.
Who should invest in Data Science?
In a world increasingly tested by false facts and fake news, I strongly believe that every student would benefit from a foundation in data science. If a general education in analytics should be part of any higher education degree, the value of a degree in data science is another question all together. If eighty-eight percent of big data professionals today have advanced degrees[v], few of these degrees are in data science. Similarly, if salary surveys underline the impressive pay scale for data scientists, having a degree in the field won’t make or break your career. Once the hype dies down, what employers are looking for are skill sets that help them solve their business problems.
Who then should apply for a specialized degree? Even if there will always be a myriad of programs ready and willing to take your money, there are prerequisites to specializing in analytics. If you don’t like solving problems, working with data, and dealing with complexity and ambiguity, you will never be a data scientist. If you haven’t taken the time in your previous studies to explore programming, statistics, and decision science, don’t fool yourself into thinking that you can now learn everything at once. You don’t become a data scientist because of a formal degree, but in developing a mindset that take to (and from work) every day of the year.
Where and when should you go to school?
There are today literally thousands of higher education programs promising a degree in data science — including seventy-four in the UK and two dozen in France. Unfortunately, many of these are simply the repackaging and marketing of existing courses from the faculties of computer science and statistics. Many tout practicums in “R”, machine learning, and data visualization — which is like offering icing on the cake without providing the ingredients to make the cake itself. A student looking for a degree program should consider the challenge as a data scientist would: what data does the school supply on the past placement of their students, what details do they provide on the course curriculum, how do they qualify their faculty, what is the nature of the required work and practical projects, and how do they demonstrate the return on your investment? -
When should you start studying data science? As a mindset rather than a diploma, analytics is developed once step at a time. Build the foundations in school, taking the relevant undergraduate and graduate programs in business, math, decision and computer science. You don’t need to enroll in a formal degree program to get started, the Internet offers a multiplicity of free online courses. Blog posts and dedicated websites offer both insight and opportunities to explore the foundations of data science. Public domain data sets and applications can help you practice detecting, exploring, and addressing various types of business problems. National and international data science competitions, like the upcoming Queen’s University Innovation Challenge[vi], offer ample opportunities to network and develop your skill set and notoriety. In this era of digital ubiquity — when is now!
Looking to enhance your data science skills? In our Summer School in Bayonne, as well as in our Master Classes in Europe, we put analytics to work for you and for your organization. The Institute focuses on five applications of data science for managers: digital economics, data-driven decision making, machine learning, community management, and visual communications. Improving managerial decision making can make difference in your future work and career.
Lee Schlenker is a Professor at ESC Pau, and a Principal in the Business Analytics Institute http://baieurope.com. His LinkedIn profile can be viewed at www.linkedin.com/in/leeschlenker. You can follow us on Twitter at https://twitter.com/DSign4Analytics
[i] Davenport, T. and Patil, D.J., (2012) , Data Scientist, the sexiest job of the 21rst Century, HBR
[ii] Ward, M. (2017), Google billionaire Eric Schmidt says this is the skill employers will look for in the future, CNBC
[iv] Levine, D. (2015); 5 Things You Should Know Before Getting a Degree in Data Science