Interview: Amit Sheth, Kno.e.sis on Designing Academic Curriculum for Data Science
We discuss curriculum development around Data Science, trends in Big Data arena, qualities sought in students and more.
Amit P. Sheth is an educator, researcher, and entrepreneur. He is the LexisNexis Eminent Scholar and founder/executive director of the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) at Wright State University. Kno.e.sis conducts research in social/sensor/ semantic data and Web 3.0 with real-world applications and multidisciplinary solutions for translational research, healthcare and life sciences, cognitive science, and others.
He is among well cited authors in Computer Science, World Wide Web, and databases. His research has led to several commercial products, many real-world applications, and three successful startups. One of these was Taalee/Voquette/ Semagix, which was likely the first company (founded in 1999) that developed Semantic Web enabled search, analysis and applications.
First part of interview.
Second part of interview.
Here is third and last part of my interview with him:
Anmol Rajpurohit: Q9. You have led the curriculum development for various certificate programs as well as courses around Big Data and Semantic Web. What do you consider the most critical components of an academic curriculum that aims at preparing students for solving real-life Big Data problems?
Amit Sheth: The certificate we now offer is called “Big and Smart Data Sciences”, and we hope it will become a degree program in near future. There are now quite a few Big Data and Data Sciences related academic programs. We wanted to focus on a subset where we could provide a unique product, and this is reflected in the use of term Smart and in the combination of Big Data with Data Science.
My MS students who have done theses have had good success in getting high quality and high paying jobs. Through this certificate, working with other excellent faculty members in Kno.e.sis and our CSE department, we want to create better prepared MS students in an area with high demand. One way we can do this is by involving students in class projects that are aligned with our excellent research program in this area. We know that recruiters and companies do pay more attention to significant projects and internships on resume, rather than a list of courses and GPA.
AR: Q10. What key trends will drive the growth of Big Data industry for the next 2-3 years and what factors will play a critical role in the success of Big Data projects?
AS: There has been a series of interesting, insightful, and provoking interviews and blogs by such stalwarts as Michael Jordan and Michael Stonebraker on Big Data. The little I could add are these points: (a) variety is a more difficult Big Data problem right now, compared to volume and velocity. The reason is that solving the variety problem typically requires human involvement (for such tasks as understanding data formats/structures/modality and their mapping to other related data), compared to other challenges where you can either throw computing power, improved algorithm or technology at the issue and arrive at a solution, and (b) both Big Data and Machine learning are at the top of the hype curve now; I think the focus will shift from data (e.g., what hidden insights in the forms of patterns can be extracted) to serving human needs that data can address in part (complemented by knowledge on the topic, etc.) but not exclusively, as is advocated by Smart Data, and Machine Learning will become an additional tool, albeit an important one, as people realize the need for complementing bottom brain type of processing (which Machine Learning supports) with top brain type of analysis.
The trend that will drive the growth will be increased and more effective human involvement in improving the processing of Big Data in comparison to current Machine Learning focused processing, and training part of the process will become more active and continuous.
AR: Q11. What is the best advice you have got in your career?
AS: It may sound strange, but I do not recall receiving explicit advice related to my career. However, I do recall soliciting advice from Prof. Ramesh Jain on whether I could develop a world-class organization at a mid-tier university (he thought so, and I believe we have succeeded as exemplified with our impact in World Wide Web). My career path has taken me from Industry R&D to academia and startups. It received its inspiration from the similar paths that Ramesh and several others took.
AR: Q12. What key qualities do you look for when selecting students for your research group or research projects?
AS: Recruiting good students was a serious business in the early days. Now-a-days, perhaps because the track record of past and current student success is rather clear, it is more a matter of selection from among those who contact me expressing specific and well-articulated reasons for why s/he would like to work with me. I subscribe to Malcolm Gladwell’s empirical study presented in the Outliers on the dominant role of desire to succeed and work ethics to go along.
Rather than GRE scores and GPA, I rely on extensive email exchanges and a video chat to understand why the student wants to do PhD, whys/he is aiming high, why s/he is willing to work to achieve the outcomes, and if I can see myself making a real difference in helping that student achieve her/his career goals. (My advisees have gone to academia, industry research labs, startups or have started their own startups soon after graduating, or industry engineering jobs). When possible, I also look for non-CS and eclectic or interdisciplinary backgrounds (former advisees who came from management, statistics, cognitive science, math and biomedical backgrounds have done fabulously well).
AR: Q13. On a personal note, what was the last book that you read and liked?
AS: Top Brain, Bottom Brain: Surprising Insights into How You Think. I liked it, but the one in this general area that I liked more and read a couple of years ago was: The Tell-Tale Brain: A Neuroscientist's Quest for What Makes Us Human.
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