Ceena was a Fellow in our Winter 2017 cohort who landed a job with our hiring partner, Capital One.
Tell us about your background. How did it set you up to be a great Data Scientist?
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.
What do you think you got out of The Data Incubator?
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.
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
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.
What is your favorite thing you learned at The Data Incubator?
I enjoyed MapReduce/Hadoop. It is an incredibly important paradigm for mining/analyzing large data sets and lays the groundwork for Spark.
Could you tell us about your Data Incubator Capstone project?
I worked on a project to identify fatal accidents and highly fatal accidents (2+ deaths) among all accidents in NYC.
How did you come up with the idea for the project?
I found the open source data set during the application process and thought about interesting questions that could provide some value to potential users.
What was your most surprising or interesting finding?
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.
Describe the business application for this project (how could a company use your work or your data)
The project could theoretically be used for the behavioral programming of autonomous vehicles or provide insights for local governments and city planning.
Learn more about our offerings:
- Find out which program is best for you – will it be our Data Science Fellowship, our Data Analytics Program or our Data Science Essentials Course?
- Hiring Data Scientists
- Corporate data science training
