How to Consolidate and Diversify in your Analytics Career

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If you belong to a technical background of any description, looking to make a move to the field of analytics is a no-brainer. It’s a rapidly growing industry, with a skill gap that’s growing at an almost equivalent pace, thus providing numerous opportunities to professionals with the right skill-set. However, it’s also worth telling the stories of people who have been working in analytics for a few years, and are looking for ways to diversify and consolidate their careers in the field. One of the students from our recent PGPDM batch, Sherin Varghese, is doing exactly this.

“After I finished my postgraduate degree in computer applications 12 years ago, I started working in data warehousing,” she told us. “I eventually made the move to banking analytics, and my most recent role was in DevOps automation. But I’ve been on the lookout for a way in which to add as much as I can to my skill set, and PGPDM is a course that’s allowed me to do exactly that.” Sherin’s decision to enrol in the program was driven by a desire to learn essential tools like R and Python, and it’s definitely paid off. While still completing the course, she was offered a job by Accenture, which she readily took up.

“I felt that I needed to learn tools like R and Python, and more importantly, I wanted a proper degree in them, rather than simply an online certification. When I heard about what PGPDM had to offer, it was exactly what I was looking for.”

“While I’ve been around data for most of my career, my skills didn’t extend beyond data warehousing and ETL,” she said. “I felt that I needed to learn tools like R and Python, and more importantly, I wanted a proper degree in them, rather than simply an online certification. When I heard about what PGPDM had to offer, it was exactly what I was looking for.” Beyond simply completing the course, Sherin said she’d also like to look into whether or not specializing in a specific area will be beneficial to her. “I’d like to learn about the entire production process of a Machine Learning model, so I’m going to do some more research on that area.”

As to the course itself, she found many aspects of it that stood out. “The course content Jigsaw provided was very exhaustive, and covered everything one would need for a space like ML and AI. The capstone project was fairly straightforward, as we knew how to apply the relevant automation processes to get the desired results. But having said that, the value of the project was also clear, especially given the insights it provided into how one would work with business data.”

As someone who’s been around data for long enough, we also asked Sherin if she had any advice for other aspiring data scientists. “I would say that anyone looking to get into data science should just take some time to know exactly what area of it they’d want to work in,” she told us. “It took me two years to finally take stock of my skills and identify the specific tools that I wanted to learn, and doing that gave me a very clear sense of direction on how to proceed. In my opinion, that’s the best way to go about it.”