Rapid Fire with PGP-BABI Alumnus Utkarsh Kulshrestha - Great Learning
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Rapid Fire with PGP-BABI Alumnus Utkarsh Kulshrestha

Your Background?

I work as a Sr Machine Learning Engineer at Kloud9. Previously, I worked for a year at TCS as Sr Business Analyst and for around 3 years at Wipro as an embedded systems Engineer.

Why Analytics?

There is an immediate need to upskill to stay relevant. Most of the existing technologies are becoming obsolete and companies across the board are adopting analytics. People who don’t learn new skills will soon be on the way out and that’s why I decided to take up a course in Analytics. Analytics and Data Science are in a growth phase so it can be very exciting to work in this domain. Salaries are higher as compared to traditional roles in the technology sector.

Why PGP-BABI?

I had evaluated around 3 different programs, but I found PGP-BABI by Great Lakes to be the best one. The profiles of the faculty members and industry guests really impressed me. It is also the most versatile program available and as a working professional, it helped me balance my academic and professional commitments.

What did you like the most about the program?

The Machine Learning module was very well structured. I also felt the domain-wise data sets are a very special feature of the program. This gave in-depth insights into the functioning of various domains. I can’t thank Professors P.K.Vishwanathan & Rajesh Jakotia enough for their wonderful sessions.

How did you bag a role at Kloud9?

After the completion of the Machine Learning module I got calls from 3 organizations for the role of Sr Business Analyst but I decided to join TCS. After working there for about a year, then I was offered a Sr machine learning Engineer role at Kloud9 and took it up. I used to practice with modelling techniques taught in the program on data sets that were available for free; this helped me understand the concepts a lot better and played a key role in bagging the Data Scientist profile at TCS.

Questions You Had to Answer in Your Interview?

Almost 80 to 85% of the questions asked in the interview were on topics covered extensively in the program. Some of the topics asked were ANOVA, modelling techniques, neural networks, Random Forest, Hypothesis testing, classification trees, etc.

Tools and Techniques You Use at Work?

I’m working on 4 different projects and these projects require me to work using R, Python, NLP, and machine learning algorithms.

Advice to Analytics Aspirants?

They should practice the techniques taught on multiple data sets and try to get a clear understanding of various business problems and how those can be solved using data.

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