This alumni interview was conducted by Liz Eggleston from Course Report.
Punam Katariya had a background in data from working as an analyst in market research and her education in math and statistics. Once she learned some programming skills, Punam decided to change careers and enter the world of Data Scientist. After doing her bootcamp research, Punam decided on NYC Data Science Academy because of their syllabus content and the exposure to real experts in the field. Now graduated, Punam tells us about the teaching style at New York City Data Science Academy, her 4 week project, and the biostatistician job offer she received after completing the course.
I had worked as a data and business analyst in market research and staffing industry respectively for total of five years. My education is in mathematics, statistics and business.
I didn’t have much professional experience as a coder. However, I was coding in C++ during my Masters program. Because of my interest in data and programming, I was looking for programs on Codecademy and Coursera.
I wanted to start my big data career as a junior data scientist. My goal from a bootcamp was to achieve hands on experience using software and learn machine-learning techniques on a fundamental level.
I chose NYC Data Science Academy because of the syllabus content. I wanted to learn modeling the data and latest modeling techniques. NYC Data Science Bootcamp had good portions of lectures about statistical models using R and Python. Also, they organized industry and field expert workshop and lectures which were very helpful.
There was a coding challenge and a personal interview, I had to go through for application.
No, I didn’t.
We were 14 people. Yes, it was a diverse group in terms of age and education background. Many of the other students left their industry to advance their career in Data Science. Some people were very good in programming already and some had core knowledge/experience.
We had two main instructors. One for in R programming and other for Python, D3 JS, Hadoop and Spark. They were always there to help and encourage students. lectures were always followed by hands on examples and homework/In class exercises. Also, all the students were asked to work on their projects during 12 weeks and present in class. Instructors, guest lecturers and guest speakers have lot of experience in their respective fields.
The course includes R, Python, D3JS, Hadoop and Spark. It was not possible for me to digest everything in 12 weeks. So, my goal was to understand the materials well and be good in at least one language.
Yes, I was satisfied, and sometimes overwhelmed, by the material.
No, there weren’t exams.
I spent more than 60 hours every week on NYC Data Science.
My first project was “Con Edison Hurricane Sandy Outage Data Presentation with R." I worked alone on this project for 4 weeks during bootcamp.
They offered interview practice sessions with professionals in the field.
I have received an offer for a Biostatistician position and paper work is in process. I was applying for jobs after completing the bootcamp. For the most part, I applied on my own. It took me three months to get a job. NYC Data Science prepared me for the interview also. However, initial material and hands on experience on regression helped me with a couple of interview questions.
I think NYC Data Science was worth the money for me. I was able get many interview calls and most recruiters were interested in discussing about my experience. I would definitely recommend it. I don’t think that Data Science can be learned so quickly on your own. At bootcamp you are learning the best practices, not only from the instructors and materials but your peers teach you a lot.
The first step in becoming a data scientist is to complete your Data Science Bootcamp Application. Just click the button to apply. It's free and will only take you about 5 minutes.