September 21, 2016

New Student Interviews: Zhanna Zhanabekova

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The new semester has just begun, and with that comes a new batch of promising Master of Science in Data Science students. To celebrate our incoming class, we decided to talk with five of the incoming students, to hear about their backgrounds, their interests, and their goals for the future. We’ll be interviewing one student each day of this week, and today we’ll be talking with Zhanna Zhanabekova.

What drew you to the Master of Science in Data Science program?

As an economist, I think that the union of economic reasoning and data science can lead to more efficient ways of analyzing markets, understanding economic behavior, and conducting academic research. I wanted to go beyond the traditional economics education, and I felt that the Master of Science in Data Science program would be a great way to merge my interests in economics, big data, and artificial intelligence. 

What did you study before coming to the Master of Science in Data Science program?

As an undergraduate student, I studied developmental economics, and a little bit of social psychology. During my graduate studies, I focused on industrial organization, game theory, applied econometrics, and monetary economics.

Can you talk about some research projects that you’ve worked on?

For my graduate thesis, I conducted research on community health centers providing affordable primary care services for those with little access to health care. Often times, individuals without insurance will wind up going to the emergency room for non-urgent or preventable conditions, and I found that community health centers can help reduce the number of emergency room visits from noninsured patients.

I think that data science has the potential to lower informational costs in healthcare, allow for more targeted cost-benefit analyses, and ultimately help patients and providers make more informed decisions.

Within the field of data science, there are many different disciplines, and an even wider set of possible applications.  Can you talk about the subsets of data science, or the data science applications that you’re most interested in perusing?

Economists live and breathe data, and yet big data has not really attracted a lot of serious interest among (academic) economists because an increased amount of data does not necessarily solve the problem of estimating causal links. However, I think there is a growing realization that incorporating machine learning techniques could strengthen economic research and allow economists to pursue new research questions.  Economists have to learn how to work with big data, and how to process unstructured data. Issues such as dimensionality reduction, selection of variables, robustness of coefficients, replicability, data storage, and interpretation of results are likely to become more prominent in the field of economics in the next few years.

I am also interested in finding ways to intersect AI and game theory in real-world applications.

What are you ultimately looking to get out of your time at the Center for Data Science?

I want to gain proficiency in the areas that I am least familiar with such as machine learning, cognitive computing, neural networks, and big data tools. Combining this expertise with my background in economics will allow me to approach real-world and research problems from a more comprehensive and diverse perspective.