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Interview: Kirk Borne, Data Scientist, GMU on Decision Science as a Service and Data Science curriculum


We discuss Kirk's role at Syntasa, the concept of "Decision Science as a Service", key components of a well-designed Data Science education curriculum, advice for young aspirants and more.



Kirk BorneKirk Borne is a Data Scientist at George Mason University. He has been at Mason since 2003, where he does research, teaches ,and advises students in the graduate and undergraduate Data Science, Informatics, and Computational Science programs. He helped to create the Data Science B.S. degree program that began in 2007. Previously, he spent nearly 20 years in positions supporting NASA projects, including an assignment as NASA's Data Archive Project Scientist for the Hubble Space Telescope, and as Project Manager in NASA's Space Science Data Operations Office. He has extensive experience in big data and data science, including expertise in scientific data mining and data systems.

He has published over 200 articles and given over 200 invited talks at conferences and universities worldwide. He serves on several national and international advisory boards and journal editorial boards related to big data. In these roles, he focuses on achieving big discoveries from big data, and he promotes the use of information and data-centric experiences with big data in the STEM education pipeline at all levels. He believes in data literacy for all.​​

First part: Kirk on Big Data in AstroPhysics and Correlation vs. Causality

Here is second and final part of my interview with him:

Anmol Rajpurohit: Q4. How is Syntasa solving the Big Data challenges differently? What does "Decision Science-as-a-Service" mean?

Kirk Borne: In the spirit of full disclosure, let me begin by announcing that SYNTASA's CEO Jay Marwaha has recently appointed me as the first member of the SYNTASA Advisory Board. I see their offering as a unique Marketing Automation solution that applies the best aspects of data science: integrating a variety of big data sources from multiple channels (including weather and location-based data, along with customer data), advanced machine learning algorithms, customer modeling, leading-edge computational technologies, predictive and prescriptive decision analytics (governed by business rules and business goals), autonomous intelligent operations, and generally a full scientific approach to the problem. The real value proposition that they bring to the field of digital marketing is this: Marketing Analytics-as-a-Service (MAaaS). SYNTASA addresses three main customer engagement pain points for marketers (customer awareness, acquisition, and retention) through the application of machine learning, decision science, and the science of optimization on an open scalable platform.

AR: Q5. There are lots of universities offering graduate and undergraduate programs in Data Science, with most of those programs having started only recently. As a distinguished professor, what do you consider as the key components of a well-designed Data Science education curriculum?

KB: I have a bias toward the science part of Data Science, but that is not the only direction one can go. For the data scientist, I encourage courses in statistics, machine learning, applied math (including linear algebra), databases and data Data Science programsstructures, data and information visualization, scientific modeling and simulation, programming (Python, R, or Matlab, at a minimum), and even some Physics (to learn and sharpen problem-solving skills). For the big data analytics profession, focus more on the algorithms (data mining, statistics, and machine learning), programming skills, and computing technologies (such as Hadoop). For the business or marketing analytics profession, include some of the above things while also learning the key concepts of business, marketing, finance, organizational management, social and behavioral science, leadership, entrepreneurship.

In general, of course, the concentrations in your own curriculum will vary naturally as your domain changes: health informatics, data-driven journalism, digital humanities, learning analytics, scientific data science (X-informatics, where X might be bio, geo, astro, or any other), or cyber-security analytics. But the core data science components are essential (databases, statistics, machine learning, and programming).

AR: Q6. Data Scientist has been termed as the sexiest job of 21st century. Do you agree? What advice would you give to people aspiring a long career in Data Science?

KB: Yes, I totally agree. I don't mean it personally, but I mean it corporately. That is, because of the attractiveness of the profession, the enormous job opportunities, the super-interesting problems that you get to solve​, the fascinating people you get to work with, the really interesting insights and discoveries that you can make, and using the coolest scientific algorithms and computing technologies on the planet, the field of data science is therefore attracting some of the best and the brightest people to the profession. That is a very good thing! And so I would say that I emphatically agree with the characterization of the Data Scientist as the sexiest job of 21st century!

My advice to aspiring data scientists is first and foremost Advice to do a personal aptitude inventory - are you good at math, do you love numbers, are you insatiably curious, do you love a challenge, are you a good problem-solver, are you a good communicator, are you a good team player, do you love the scientific approach to things, do you love digging deeper for discoveries when others might have given up, and do you want to grow and re-make yourself every few years? If you can honestly answer "yes" to these questions, then a long successful career in Data Science is yours for the asking.

If you perhaps do not have strengths in all of those areas, then identify where your weaknesses might be, nurture those talents, grow them, feed them -- then find data science activities (do consulting, apply for internships, join a team), take classes, solve some Kaggle.com problems, and push yourself. The rewards will be great, but the fun and satisfaction will be even greater.

AR: Q7. On a personal note, we are curious to know what keeps you busy when you are away from work?

KB: My wife would say that I am always working. Of course, she knows best. But, apart from my "day job" doing research, teaching, and advising students at the university, I spend a lot of time as an independent big data science consultant with businesses, plus I spend too much of my spare time on Twitter -- spreading the good news, the discoveries, the great insights, and cool stuff from the world of big data and data science, 140 characters at a time. Beyond that, we have 3 new grand-babies in the past 16 months. That is an awesome change of life experience. They are a fantastic joy and I am happy to be busy with them at any time.

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