“The Data Science Program has a great network connecting individuals to different data science opportunities on campus. This past year, I was able to go to the final round of the Citadel Data Challenge partnering with other students in the program.”
Degree(s):
B.S.E in Networks & Social Systems Engineering, University of Pennsylvania
Hometown:
Florham Park, NJ
What was your background before joining the Data Science (DATS) Program?
I am currently an undergraduate in the Networks and Social Systems Engineering Program at Penn. Throughout my first two years at Penn, I took many computer science and statistics courses enjoying topics such as Databases, Statistical Inference and Artificial Intelligence. Over my first summer, I worked at a veterinarian startup, creating a pricing and recommendation engine to better aid customers in understanding the patient-lifecycle of their pets. This past summer, I worked at a real-estate company building financial models that predicted prices of mortgage back securities. I have also worked on side projects including a messenger bot that allows users to order pick-up from local food trucks.
What drew you study Data Science?
My favorite projects have always been to build technologies that connect individuals to information. Data science to me seemed to be a perfect combination of computer science and statistics, of both collecting relevant data and conducting meaningful analysis. My projects would consist of both information extraction either from a website or a database, as well as using machine learning and statistical techniques to make useful predictions. Whether it is helping companies understand where to open up their next store, or giving customers the knowledge they need to be smart about their purchases, I love creating and making data-driven decisions.
Why the MSE in Data Science at Penn?
I have always liked a wide variety of subjects and fields, spanning from physics to medicine to economics. As an engineer and computer scientist at Penn, I got the opportunity to apply my computer science and data science knowledge to these many fields through performing research at a neuroscience lab and working on a customer analytics project for URBN. Penn SEAS truly caters to individuals that are keen on exploration, as there are so many interdisciplinary opportunities spanning throughout the entire university. I now get the unique benefit and intellectual freedom of crafting my education and am able to take courses within all four schools and specialize in the areas that I find interesting.
In what ways has the Data Science Program helped you to reach your goals?
Data Science has recently become a very desirable and marketable skill to have. This discipline has allowed me to explore multiple areas of interests and multiple different job opportunities in industries such as finance, technology and healthcare. The Data Science Program has a great network connecting individuals to different data science opportunities on campus. This past year, I was able to go to the final round of the Citadel Data Challenge partnering with other students in the program. I also get the opportunity to work on a practicum project allowing me to apply my skills to medical data.
What did you do to relax and have fun outside of school?
My largest extra-curricular activity is to be a dancer on Penn Masti, a south Asian fusion dance team. As a team, we practice a couple of times each week in preparation for our national competitions. We also have an annual show in the spring that is open for the Penn community. In addition to dancing, I am also on the honor council here at Penn and work on planning events to spread integrity around campus as well as adjudicating select disciplinary hearings. Other than those activities, I enjoy exploring new restaurants in the Philadelphia area.
What advice do you have for prospective students on the application process?
Understand the fundamentals. There is a lot of jargon in the data science world and it can be confusing. I recommend reading and practicing a lot, whether that is playing around with open data sets online or creating a new data set and experimenting with that. While experimenting with new data it is important to get new perspectives. I had the great opportunity of taking similar data science/ machine learning classes from different professors and from different schools (Engineering and Wharton). This allowed me to get a variety of perspectives and techniques to apply to new data science challenges.