Learning to Think Like a Data Scientist: Alumni Spotlight on Ceena Modarres

I received my M.S. in Reliability Engineering from the University of Maryland. In Reliability Engineering, a practitioner will assess/prevent the failure of a physical system (car, computer, etc.). Many of these approaches tend to be statistics and data driven and much of the modern research in the field (including my own) uses Machine Learning to improve relevant analyses. However, when I was done with my Master’s, I realized I was more passionate about the Data Science/Machine Learning than the engineering side. So when I heard about The Data Incubator, it seemed like a great fit.

As a recent MS graduate who had never worked before, the most important thing I learned at The Data Incubator was how to think like a Data Scientist. Since Data Science is still a new field, many positions require a unique and not necessarily homogenous set of skills. The Data Incubator not only teaches its students all the necessary technology, but it teaches them how to think about Data Science problems in a systematic and effective way. TDI also provided a network of possible employers and former alumni that proved valuable for my job search.

Give it your all. The application process is multi-round and selective because Data Science job searches are also that way. But, if you take the time to try to consider the perspective of prospective employers in your application process it will work out.

I enjoyed MapReduce/Hadoop. It is an incredibly important paradigm for mining/analyzing large data sets and lays the groundwork for Spark.

I worked on a project to identify fatal accidents and highly fatal accidents (2+ deaths) among all accidents in NYC.

I found the open source data set during the application process and thought about interesting questions that could provide some value to potential users.

I think the most relevant factor was geospatial coordinates. In particular, some intersections tend to simply lend themselves to fatal accidents far more frequently than others. Thus, as always, there is some information/explanation that remains missing.

The project could theoretically be used for the behavioral programming of autonomous vehicles or provide insights for local governments and city planning.