Journey From a Mainframe Tester @Infosys to a Data Scientist @Walmart
Before you read this blog, I would like you to write on a piece of paper and ask yourself “Why do I want to be a Data Scientist?”
Background
I completed high school in 2011 with no computer science background and graduated in 2015 as an Electronics and Communications Engineer not from a Tier 1/2 university in India. Though the university tier is of less relevance in the current scenario, however, many people are still of the impression that their career is defined by the college/university they are studying at.
During the 4 year graduation, we were taught C programming language, DBMS, and Data Structures but I did miserably in all of them. At that time AI/ML was a term I never heard of. It was about getting a job at a service-based IT firm and that’s what happened during campus placement where I joined Infosys as a fresher.
Early Career
After clearing Infosys training, I got mapped to a Mainframe project where I was working as a tester. I used to hear a lot of voices saying that if you do not get a development project, your career is doomed. However, I didn’t pay much heed to them and continued with my work.
After a year or so, I didn’t enjoy working in Mainframes and started looking for the project internally. Luckily, I got a chance to work in SQL, Hive and got some exposure to Big Data. It was during that time I came across the term Machine Learning. And that was my eureka moment.
Infosys to Walmart
I spent close to a year interviewing for multiple companies between 2018 and 2019 before I managed to clear one and joined Tiger Analytics as a Senior Analyst. Before joining Tiger, I already worked in Machine Learning/Deep Learning at Infosys for some time. However, the work, culture, and people at Tiger gave me a whole new perspective in the world of Data Science.
After working close to 2 years, I felt ready for my next big move and that is when I landed at Walmart Global Tech India as a Data Scientist. Before joining Walmart, I spent a few months at a startup but this opportunity was too big to pass by.
I recently gave an interview with Applied AI where I shared my experience. You can watch it here.
Preparation Strategy
When I got to know about Machine Learning, the first course I took was Andrew Ng’s Machine Learning from Coursera. There are a couple of other MOOCs that I did. One was from Udemy and the other from Udacity.
However, I was always consistent in reading blogs from Medium, Analytics Vidhya, or KDnuggets. I participated in various hackathons during my weekends to practice different techniques and approaches.
My Unbiased Advice
- Your University Tier doesn’t matter. If you have skills, the sky is the limit.
- Build your foundations first before jumping into advanced methodologies. Most companies work with simpler models.
- Do not ignore SQL. I repeat do not.
- Pick any language (Python/R) and start exploring data with it. In the longer run, it is irrelevant which one you choose.
- Once you follow steps 1–4, pick your area of interest (NLP, CV, Time Series, etc) and start reading articles, papers. Download open-source datasets and try implementing what you learn.
- There are tons of free resources available online that you could refer to. In the initial days, no need to spend too much on paid courses. I would be sharing some of my favorite free resources in a separate blog.
- Being a Data Scientist is a lifelong journey. It’s a marathon, not a sprint. Please do not beat yourself up if you are not a Data Scientist in 3 months because no one is.
- Practice. Practice. Practice.
End Notes
I hope you have got your answer “Why I want to be a Data Scientist” by now. If you have, then start your journey today.
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