My path towards data science

My interest in “data science” dates back to graduate school at NYU, before I knew what data science was, much less that it was a thriving industry with a multitude of opportunities.

I received my Masters in Public Administration with and focus in finance from NYU with my goal when I started the program to work in investment banking for public entities or as a credit analyst at one of the big ratings firms.During graduate school, a sizable portion of the course work was in statistics and my favorite course was “Multiple Regression and Econometrics.” The coursework required constructing and analyzing statistical models in Stata.

The program culminated in a Capstone project where my team performed a statistical analysis of the provision of the Low Income Housing Tax Credit, to evaluate its impact on private housing construction in the U.S. To estimate crowd out our team utilized both an OLS design and instrumental variable design to provide an estimate of any causal relationship.

I found my way into financial risk management and enjoyed it, but after a few years working in risk, I desired a closer proximity to my family in Texas and relocated to take a role as a Financial Analyst with a software company in Austin.

However, rather than bearing resemblance to the quantitative and statistics-heavy risk management roles I held in New York, I quickly discovered the role was closer to that of an accountant — akin to what one mentor would later describe as “balancing a checkbook.”

Fortunately, part of my role required construction and maintenance of dashboards to provide monthly performance metrics to business leaders across the company, often using unstructured data from disparate sources. I loved cleaning the data and consolidating it into a presentable format to tell a story about the business. In time, my role expanded to working with a data architect within the IT organization, who was building a data warehouse for the company. Due to my familiarity with the organization’s data I was responsible for quality assurance on the data warehouse — ensuring that what the architect created was accurate and tied back to the source system.

When I identified exceptions or clean up opportunities, I would sit with the data architect so that she could take my requests and update and run her code so that we could visualize the results together.

Watching my colleague update her code was exciting and I could feel my eyes widening as she made changes. It was exhilarating watching a code being put together according to specifications I was offering so that we could achieve a desired outcome — accurate information in the new data warehouse.

And these experiences got me thinking. The day-to-day work as a “financial” analyst was not very exciting for me, but getting my fingers dirty with the vast amounts of data was. Watching someone else code and build a program to handle vast amounts of data to help the business was exciting and I wondered if I could take my career in a new direction.

These bits and pieces of exposure to the world of data — building dashboards, coordinating with a data architect to ensure accuracy of the data warehouse and my understanding of how data was driving decisions at my company — and my growing fascination with this world — inspired me to research the data analytics and data science industries and explore the possibilities.

My research uncovered that “data science” is a field that uses computer programming and statistics to derive insights from the vast amounts of data points in the world to make predictive insights — something that sounds incredibly similar to what I was looking for after graduate school.

I decided to explore more and see if a career change toward data science was something that I truly wanted to pursue. I discovered a website called “DataCamp” with a reasonably priced subscription that allows users to choose a “track” and learn data science in their preferred language, in my case Python. As I progressed through these “tracks” my excitement and passion continued to grow. Not that learning was without its difficulties or frustrations, but the satisfaction of writing a code to obtain a desired outcome was exciting and euphoric.

Knowing that I enjoyed the DataCamp exercises and confident that I could learn Python, I asked myself — was I content to learn on my own, part-time, while continuing at a job that I was not passionate about and felt like it had limited growth opportunities, or should I find a way to build my skills and data science and pursue it full time?

I posed my dilemma to my data architect colleague and she encouraged me to jump into data science with both feet, and a few mentors at my company offered some amazing advice. One couldn’t be more excited that I was considering the switch, emphasizing the growth and pace of the industry that would create fruitful opportunities. Another pushed me to find pursue a passion, encouraging me to break away from being “just another cog in the accounting wheel.”

The advice I received further stimulated my desire pursue data science full time. I knew there would be a steep learning curve, so I researched the best ways to tackle this and learned full time, in-person bootcamps were a great option. I researched available bootcamps — both online and in-person and found a great option at General Assembly in Austin. GA offered an immersive program designed to teach students Python and learn critical data science concepts while also building a portfolio of projects to showcase to prospective employers. I had already heard positive feedback about GA from college friends who had taken GA’s software development courses, and my research on their Data Science Immersive program, combined with great conversations with their admissions team, helped determine that GA was the right program at the right time to pursue.

I am excited about what the future holds and will use this account to share what I learn on my data science journey and document exciting discoveries and how I overcome many challenges I expect to encounter along the way.

Data Scientist. Problem Solver. Solutions Advocate. Finance Nerd.