This article was originally published by Ashutosh Agrahari (UpGrad Data Science Program Alumnus) on LinkedIn Pulse
I am a Data Analyst working in Gain Theory, a WPP company based in Bangalore. Right after doing my M.Tech in Computer Science from Pondicherry University, I joined TCS. There I worked as an analyst in the testing domain (manual testing). After completing more than 1.5 years in the software testing domain, I was very uncertain about my future. My job role didn’t match my educational qualifications nor my research work in soft computing. So, I started looking out for re-skilling opportunities in advanced data analytics techniques with open source programming.
I came across many online courses in data science with fees varying from INR 30,000 to 5,00,000.
I researched for around 2 months; conducting a detailed analysis of all the courses available in the market. Then, I decided to enroll in the UpGrad program due to the course structure and affiliation with IIIT-Bangalore. The decision to choose UpGrad wasn’t easy as it was the first batch and did not have a detailed course review.
the next biggest thing
The UpGrad course structure is very well suited for people who don’t have experience in statistics and data analysis using R programming. Here, I learned the basics of statistics as well as advanced data science techniques. We had several webinars with industry experts along with the weekend online classes from IIITB Professors. I learned the real implementation of analytics in Industry and bridging concepts and their implementation gap. Each topic had an individual case-study and a group case study which helped me understand the solution in multiple ways. UpGrad has an excellent student support team as well, who was always ready to resolve issues related to the program.
There were times when I used to spend around 4-5 hours a day, along with 9 hours office time, studying. I was spending around 20 to 25 hours a week on this course. The course was rigorous, we had to submit the case studies in a short time. It helped me to learn fast and implement learnings from our real industry projects, in a smarter way.
My transition as a data analyst wasn’t easy. 5 companies interviewed me before I got selected by Gain Theory. I faced many problems in interviews and many concepts that I had learned in analytics I was unable to explain mathematically to the panel. Most interviewers were keen to know the mathematical and statistical concept behind an algorithm and not just the usage of R command to run a model. So, I started to focus on mathematical concepts, which helped me to secure the job at Gain Theory after 6 rounds of interviews including one case study.
Currently, I am working in an R&D team for product development for Market Mix Modelling using cases in marketing analytics. We have been able to build the model for 1000 variables with a different kind of regression model like OLS fixed effect, mixed effect using the Shiny web app for UI design.
All data science enthusiasts should have knowledge of data management, data analysis, statistics, business domain and especially the interpretation of model results. UpGrad’s team, along with IIITB professors, are excellent in their approach. I think people in the IT industry should think about the future of IT after 5 to 10 years where every decision maker will use data. Machine Learning, Automation and IoT will all require data, which is the future. We need to upgrade ourselves to be relevant in the industry with data science and analytics as the next big move.
The UpGrad team has been extremely helpful throughout the course. I really appreciate their effort in delivering quality and value to the course. Their career support team is also very efficient. They provide enough support for interviews, including personalized support for interview preparation, resume formatting, etc. I really thank UpGrad for helping me in my transition as a data scientist.
Everybody should make their own path to achieve their dream. Sometimes all it takes is a little help from others who have been through the same to save added time and effort.