Aurora was a Fellow in our Spring 2016 cohort who landed a job with Verizon Wireless.
Tell us about your background. How did it set you up to be a great Data Scientist?
I obtained my Ph.D. in Neurobiology and Behavior from UC, Irvine in 2014. I collected data related to brain activity representing autobiographical memory using Magnetic Resonance Imaging (MRI) for my dissertation. The accurate analysis of MRI data demanded the ability to preprocess, and clean data as well as automate the processing steps using Matlab and R. Understanding how to properly use these tools was instrumental towards acquiring a new programming language (i.e. Python). Furthermore, the ability to apply statistical concepts to analyze various forms of data from diverse scenarios was highly conducive towards becoming a well-rounded data scientist who excels at analyzing novel datasets.
What do you think you got out of The Data Incubator?
What advice would you give to someone who is applying for The Data Incubator, particularly someone with your background?
Data science is an extremely broad field. The Data Incubator provides a curriculum that captures the most important concepts in data science, and provides direction in a field that can be overwhelming due to the amount of information it encompasses. I would advise that incoming fellows obtain an understanding of which areas excite you most (e.g. Machine Learning, AI, Natural Language Processing, etc.). I would then build a solid foundation of math/stats/probability and dive into the area you find most enticing.
What is your favorite thing you learned at The Data Incubator?
My favorite and most useful concept learned at The Data Incubator was how to appropriately apply Machine Learning techniques to accurately predict outcomes using Python.
Could you tell us about your Data Incubator Capstone project?
A major concern for the lack of transportation options for senior citizens has been expressed by community and government officials alike. I, therefore, used data science techniques to create an interactive map that would help ride-share companies locate and better understand the type of disability the senior citizen they are picking up may have. This would enable drivers to provide an ideal experience to the seniors they pick up. Senior citizens, in turn, would have a greater ability to get around their neighborhood, which could consequently reduce their social isolation.
How did you come up with the idea for the project?
I worked with socially isolated and lonely senior citizens during my previous position as Head of Research at a tech. startup called GrandPad. The impetus to provide seniors a viable solution that would allow them to easily get around their neighborhood stemmed from the fact that loneliness and social isolation are predictors of many of their adverse health outcomes.
What technologies did you use and what skills did you learn at TDI that you applied to the project?
I used SQL as well as Python and related tools (Pandas, scikit-learn, etc.).
Describe the business application for this project (how could a company use your work or your data)
Ride-share companies (e.g. Uber, Lyft, etc.) view the problem of senior social isolation as an opportunity to grow their clientele. Using my map, ride-share companies can locate districts that are densely populated with senior citizens and provide an ideal experience for seniors as they will be prepared with the knowledge of the type of disability the senior citizen is likely to have.