Maryland Smith: Imparting the Best Data Science Education to Build Data-Driven Professionals

Big data analytics tools and platforms are undergoing significant disruptions in functioning with the amount of data generated today. Enterprises are struggling to deal with new types of data and understand which information can potentially provide value to the business. Recognizing the same, the University of Maryland's Robert H. Smith School of Business has designed a Master's Programs in Analytics which covers a spectrum of tools and techniques to tackle big data challenges faced by enterprises. In an interview with Analytics Insight, Wendy Moe, Associate Dean of Master's Programs at Maryland Smith explains how the program helps students master skills that are necessary to thrive in this data-driven world.

What is the edge your Analytics and Data Science Program has over other institutes in the industry?

The business environment is evolving rapidly to be more data-driven. To succeed in today's business environment, one needs to not just understand where the data comes from or how to analyze the data, but also how it fits into the decision-making process.  In this data-heavy business environment, successful organizations know how to leverage data to develop strategies, serve customers, and measure success. Maryland Smith is uniquely positioned to provide students with data-driven decision-making competencies.  Our faculty is some of the best in the world in this area, and many are renowned researchers who have spearheaded the innovation in data-driven decision-making.  We also have a strong presence in the Washington D.C. region, where both private and government organizations have invested heavily in analytics, data sciences, machine learning, artificial intelligence and other business technology innovations. And, Maryland Smith partners with these organizations through our Center for Digital Technology, Analytics, and Data Sciences, offering students outside-of-the-classroom experiences and networks.

Kindly brief us about your role at the Institute/ University's Analytics and Data Science Program and your journey in this highly promising sector. 

I am Associate Dean of Master's Programs, Dean's Professor of Marketing and Co-Director of the Smith Analytics Consortium at Maryland Smith. My research into online marketing and social media feeds my recent book, Social Media Intelligence.  And, I serve as Co-Editor of the Journal of Interactive Marketing and on the editorial boards for Journal of Marketing Research, Marketing Science, and Journal of Marketing. I also have consulted for numerous corporations and government agencies, helping them develop and implement state-of-the-art statistical models in the context of web analytics, social media intelligence and forecasting.  And I have been an expert witness and consultant for litigation related to online retailing, advertising and branding issues.

What are some of the challenges faced by the industry today? 

The biggest barrier I see to capturing value from data and analytics is that there is too much data.  With so much data, one can drown in metrics and lose sight of the big picture business objectives.  Organizations need to stay focused on their strategy and objectives and concentrate on the key metrics that help them make decisions relevant to those strategy and objectives.  It's easy to get distracted by the flashy dashboards and all the curious metrics they provide, but this can often be the downfall of an organization as it distracts managers from the big picture and over-focuses them on short-term tactics.

What are the skill sets that you specifically look in students before admissions?

A strong quantitative ability and interest.  An ability to think analytically and innovatively.  There needs to be an attitude through which students believe that they can adapt to the rapidly evolving business environment and contribute to its future evolution. 

Can you brief us about curriculum and pedagogy?

Our curriculum is a blend of strategy/framework courses and quantitative modeling courses. The goal is to teach students the quantitative skills and how to apply them in their business context as the business world continues to evolve.

What will the participants gain from the programs?

Students will gain analysis skills (statistical modeling, machine learning, data mining, etc.) as well as technical skills (R, SQL, Python, etc.).  They also will learn the business frameworks and theories and how to appropriately apply their analysis and technical skills in various business situations.

How will the programs be delivered/taught? Is there any industry participation?

Many of our classes integrate traditional lectures with hands-on labs where students work on a real-time simulation, analysis of a real dataset, etc.  Classroom work also is supplemented with co-curricular activities.  For example, students also have the opportunity to engage with the Smith Analytics Consortium, which hosts thought leadership conferences, partners with businesses, and sponsors a datathon competition where student teams analyze a dataset with the coaching of industry and faculty mentors and present their findings to a panel judges.