From Philosophy to Data Science
Iman Singh made the transition to data science from a background in philosophy. He spent a few years working in the nonprofit sector. Then, intrigued by the powerful impact data analysis could have on decisions, he resolved to study the science behind it. That curiosity is what led him to enroll in the NYC Data Science Academy bootcamp. Now he is applying the skills he learned at the bootcamp in his new job on the data science team at Gryphon Strategies.
What was your educational and career background before you decided to pursue data science?
I majored in philosophy at Columbia University and continued on there for a master’s degree. After that, I worked in executive support roles at nonprofit organizations and also taught high school.
Why did you decide to study data science?
I became interested in data science when I recognized the power of analytics and statistics in making data-driven decisions. Seeing a decision being made that pointed to data made a big impression on it. It seemed like a very powerful role to be able to understand how these statistics were created and how to work with the raw data to analyze them to make them interpretable for decision makers.
How did you go about making the transition into a data science career?
I began my education in the field by taking some online MOOCs in programming, data analytics, and data science. I then decided to make the transition for real by quitting my job and joining the New York City Data Science Academy bootcamp.
Why did you select NYC Data Science Academy?
When I made the decision, I researched bootcamps extensively. I looked at LinkedIn profiles of graduates. I looked at online reviews. I tried to assess the reputation of various bootcamps and also the curriculum to be sure they covered all the essential tools. I also looked into the faculty who were teaching the students. In all these areas NYC Data Science Academy appeared to do really well. All my expectations were met when I spoke with representatives from the schools during the application process.
How did you find the other students?
My cohort had really great students in a range of ages and background. A number of them already had PhDs. I learned from each and every one of them. It was wonderful to be in such a rigorous environment, working hard with these supportive and great people.
How did you find the curriculum?
The curriculum was very rigorous, which is what I wanted. They teach you SQL, as well as both R and Python. I really appreciated learning machine learning tools in the two languages. That way we were able to reinforce the machine learning tools and understand how the statistics would be implemented in multiple languages.
How did you find the projects you worked on during the bootcamp?
I worked on four projects as part of the NYC Data Science Academy. Two were individual projects. One was done in a group of four people and the other in a larger group of eight people. My favorite project was the web scraping one. I found it to be a powerful tool to be able to go out there and grab data. I ranked tennis string and then used that tool to find my own set. So it helped me in a personal way, and it was really fun to work on that data set.
Did you find that the projects helped prepare you for real life data applications?
Yes, I really appreciated that the academy allowed me to work on a real-world project sponsored by an industry partner because it allowed me to gain client interaction and also see how people in industry store their data, set up their data pipeline, and how they access it.
For my capstone project, I used social network analysis techniques to help a startup music rating community understand the interactions between artists and critics on its site, identify the most influential users according to various centrality metrics, and recommend songs to play and other users to connect with. This experience with network analysis proved to be very helpful in my job search, as I was able to refer to it in my interview with the company that hired me.
How did you go about your job search, and in what way NYC Data Science Academy help you?
It helped with resume preparation and the job search in general. For the time in which I was working on finding the right job, I applied to as many as I could while continuing to work on my projects so that I was able to keep working in the field and keep honing my skills and also to have things to speak about during the job interviews. I structured my day about half and half, applying for new jobs and also developing my skills.
Can you tell us what you do in your job?
I’m a Senior Data Analyst at Grypon Strategies. The firm has financial and legal firms as clients. It offers fraud investigation, asset tracking, due diligence and other litigation support work for clients. I’m working on a small but growing analytics team. I’m really excited to be working on these very interesting data sets. One of the cool things about the position is that I get to do network analysis. I did network analysis with an industry client during the bootcamp, and being able to refer to that experience was very helpful during the interview process.
What advice would you have for students considering enrolling in the data science program?
Work hard. Keep up with the homework. Make sure that you really learn the basic data wrangling, data manipulation, and data visualization skills and focus on those because those will be used in every project. You have to clean the data and prepare it for analysis. Those are core skills.
This post was first released on SwitchUp.
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