How universities and professional institutes can benefit from bridging the data scientist gap

Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions.

Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in?

It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution.

But Data Science practice, which involves advanced analytics implementation, with a more deep-level exploratory approach to implementing Data Analytics, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Prescriptive Analytics areas would require a more establishment-centric, dedicated and extensive curriculum approach. A data scientist differs from a business analyst ;data scientist requires dwelling deep into data and gathering insights, intelligence and recommendations that could very well provide the necessary impetus and direction that a company would have to take, on a foundational level. And the best place to train such deep-seeded skill would be a university-led degree course on Data Sciences.

It’s a well-known fact that there is a huge gap between the demand and supply of data scientist talent across the world. Though it has taken some time, but educationalists all across have recognized this fact and have created unique blends of analytics courses. Every month, we hear a new course starting at a globally recognized university.

Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students.

Strategic Benefits to implementing Data Science Degrees

Data science involves multiple disciplines
The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. They might involve computer science, mathematics, statistics, business understanding, insight interpretation, even soft skills on data story telling articulation.

Beware of programs that are only repackaging material from other courses
Because data science involves a mixture of skills — skills that many universities already teach individually — there’s a tendency toward just repackaging existing courses into a coveted “data science” degree. There are mixed feelings about such university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them. Much of a program’s value comes from who is creating and choosing its courses. There have been some decent course guides in the past from some universities, it’s all about who designs the program and whether they put deep and dense content and coverage into it, or whether they just think of data science as exactly the same as the old sort of data mining.

The Theories on Theory
A recurring theme throughout my conversations was the role of theory and its extension to practical approaches, case studies and live projects. A good recommendation to aspiring data scientists would be to find a university that offers a bachelor’s degree in data science. Learn it at the bachelor’s level and avoid getting mired in only deep theory at the PostGrad level. You’d think the master’s degree dealing with mostly theory would be better, but I don’t think so. By the time you get to the MS you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers.

Hence, universities, especially those framing a PostGrad degree in Data Science should make sure not to fall into orchestrating a curriculum with a long drawn theory-centric approach. Also, like many of the MOOCs out there, a minimum of a capstone project would be a must to give the students a more pragmatic view of data and working on it. It’s important to learn theory of course. I know too many ‘data scientists’ even at places like Google who wouldn’t be able to tell you what Bayes’ Theorem or conditional independence is, and I think data science unfortunately suffers from a lack of rigor at many companies. But the target implementation of the students, which would mostly be in corporate houses, dealing with real consumer or organizational data, should be finessed using either simulated practical approach or with collaboration with Data Science companies to give an opportunity to students to deal with real life projects dealing with data analysis and drawing out actual business insights.

Don’t Forget About the Soft Skills
In an article titled The Hard and Soft Skills of a Data Scientist, Todd Nevins provides a list of soft skills becoming more common in data scientist job requirements, including:

  • Manage teams and projects across multiple departments on and offshore.
  • Consult with clients and assist in business development.
  • Take abstract business issues and derive an analytical solution.

The article also emphasizes the importance of these skills, and criticizes university programs for often leaving these skills out altogether: “There’s no real training about how to talk to clients, how to organize teams, or how to lead an analytics group.”

Data science is still a rapidly evolving field and until the norms are more established, it’s unlikely every data scientist will be following the same path. A degree in data science will definitely act as the clay to make your career. But the part that really separates people who are successful from that are not is just a core curiosity and desire to answer questions that people have — to solve problems. Don’t do it because you think you can make a lot of money, chances are by the time you’re trained, you either don’t know the right stuff or there’s a hundred other people competing for the same position, so the only thing that’s going to stand out is whether you really like what you’re doing.

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