However, my undergrad was in ECE and my work experience was only in Database Management Systems, which is a tiny subset of the data universe. Exploring the vast realm of Data Science needed giant leaps and I did not know where or how to start.

Although I spent most of my first semester confused, I used to listen to the ‘Super Data Science’ podcast when I walked to Purdue’s Earhart Dining Court for my part-time campus job. The episodes cover a wide range of topics. The guests include data science nerds from aspirants to experts. It gave me a better understanding of the tasks they did and the problems they solved every day. I heard repeatedly that an ideal Data Scientist has business, statistics, and programming skills. Fortunately, the MEM program allows us to take courses from multiple departments. I handpicked courses from different departments to work on each of these skills. I built basic programming and analytical skills from online resources. With those skills in my arsenal, I started doing pet projects. Having built analytical models, I got a chance to work on a data project with Purdue’s Dauch Center for the Management of Manufacturing Enterprises. In summer, I participated in a Krannert Case Competition where I got a chance to network with people who shared my obsession with data.

I secured an internship through one such connection, a Purdue alum, who was assigned to my team as a mentor. When we realized that we share the same passion for data, I asked him if he could mentor me for a project. After delivering the results, I expressed my interest in working for his company. Initially, he said he was not sure if could hire an intern and offered to refer me to one of his colleagues from bigger companies. When I politely declined his offer, he was taken aback. I had displayed my skills through my project, and I showed commitment to his company. After a week, he sent me the offer letter for the internship. During the internship, I got a chance to work with real world data and with a diverse set of clients. I extensively used Python and learned Power BI, which is one of the most desired tools in analytics.

In the following semester, I worked as a Teaching Assistant at Krannert for two courses, Python Programming and Management of Organizational Data. As programming and DBMS questions are often asked during interviews for Data Analyst/Data Science roles, I now feel more confident while facing interviews. Right now, I am reading papers from top Natural Language Processing conferences to get acquainted with the state-of-the-art Data Science technologies.

In hindsight, my learning journey, much like a Machine Learning model, has been iterative. The key is to get a little closer to the expectation with every iteration. Every giant leap is just a combination of many small steps.