At INSAID, we create accomplished and empowered Data Leaders. We groom our students to dominate the world of Data science and Artificial Intelligence and reshape their future. We value what our students bring to the table. We share their vision and support them during their journey and ensure that they carve a niche for themselves.
We’re proud to have tutored exceptional students all across India. Today, one such exceptional student, Amoolya Shetty stands in the spotlight.
Student Name: Amoolya Shetty
Batch: CDF – February 2019
Total years of experience: 2 years 7 months
Area of expertise: Application development
Malvika: Amoolya, we can start the interview with you telling me something about your professional journey.
Amoolya: Yeah, sure. I am a computer science engineer, graduated in 2016. Post-college, I immediately joined Accenture as an Application Development Analyst. In total, I have about 2.7 years of experience till date.
My very first assignment at Accenture was a Google project- basically a supply chain management project. I worked as a Technical Support Analyst, wherein I provided support and maintenance to a suite of applications in the supply chain management domain. Google basically has 4-5 verticals in the supply chain, of which I worked in 3, those being Google Consumer Hardware (B2B sales), Google Data Center and Google Fiber (gFiber).
Malvika: So what got you interested in data science and machine learning?
Amoolya: Oh, that’s actually an interesting story. It roots back to my engineering days, I attended one conference call at HIPC or high-performance computing. This is a symposium wherein people from different companies like Google, Microsoft, and also students from various colleges come together to talk about various trending technologies. So that opened up many avenues for me, of which one was deep learning and machine learning. It was just a buzzword for me back then and I didn’t know much. Still, I was really interested to know more about this, I got into this boot-camp.
In the boot, we were exposed to machine learning platforms, like Caffe and torch and they gave us some project to work on. It was based on image processing. They instructed us well until completion. That’s actually what intrigued me to get into this domain. After that at Accenture, everybody kept talking about data science as well.
Malvika: Amoolya, what is the goal of data science?
Amoolya: Let me start with how data science has been evolving in the past few years. If you see the graph of data science has always been rising.
The graph of data science will always go up. We’ve been sitting on huge reserves of data for so long and now is the time to put it to use. As long as you’re in an industry that has been collecting data, data science will play a big role for you.
Data science has a hand in every field; be it medical, be it political. Everybody across industries is actually on the lookout for data scientists these days.
Malvika: Are there any applications of machine learning that have got your attention?
Amoolya: So I follow Casey, she’s a machine learning instructor in Google and I follow Andrew Ng on medium. A while ago, I read an article where Casey explained about applied machine learning and machine learning research and the difference between the two.
So we have two sets of machine learning scientists, basically, one is into research work and their main purpose is to build machine learning algorithms. The other is into applying this machine learning in real-life situations.
In this space, I’m most excited about personal assistants, like Google Assistant. Using artificial intelligence and machine learning to create something like that’s so handy and comfortable, that keeps me interested.
Malvika: INSAID’s mission is to Groom Data leaders of tomorrow. What do you
understand by a Data leader? And how is a Data leader different from a Data
Amoolya: Let me explain in layman terms. Imagine a Venn diagram with two sets: statistics and programming. At the intersection is where you find an actual data scientist. A data scientist should have skills in both the spheres. That’s how you become a data scientist.
What sets a Data Leader apart from the rest of the Data Scientists is that as a leader, you guide your team in the right direction. As a data leader, you should be able to elicit insights from the existing data and apply them to commercial, societal, and practical use.
Malvika: What were some initial challenges that you faced when you started on your journey of data science? Based on that, do you have any advice for a total newbie?
Amoolya: There were some new things for me to tackle when I started. Even though I come from a computer science background, learning Python was a new subject for me. It was a challenging task, right?
Another was making sense of all the data. Getting started was really a challenge for me, I kept wondering where do I start, where do I jump?
My classes helped me get some structure. I understood data handling requires a step-by-step process like cleansing, processing, profiling and then post profiling. Going from absolute raw data to generating charts and then storytelling is a beautiful job.
You have to understand practice is something that would make anyone perfect. So for any newbie out there, I would say, don’t be apprehensive. Don’t get overwhelmed with the data that you have. Some structure and practice will get you where you want to be.
There are so many technologies out there- Stack Overflow, GitHub; there are unlimited resources! I had a GitHub profile since college, but I understood the full power of it at INSAID.
Also, interest is a very important factor, also underrated. That’s an X factor that will help you hold on.
Malvika: Amoolya, crafting a great data science resume is a critical part of getting shortlisted for Data Science roles. Can you tell me some ways in which you have improved your resume
as part of Data Science Career Launchpad?
Amoolya: Sure. A very important thing in that session was the distinction between what to put in a resume and whatnot. Most of us fill our resumes with such jargon without understanding the purpose.
It’s not enough to master a topic, have the technical know-how but also how you project it. Another interesting insight from the Career Launchpad was the use of infographics, since we’re budding data scientists know, an actual career-graph would be a clever way of presenting your case. Suppose I have 2 years of experience in one domain and another year of experience somewhere else, a more visually arresting way would be to use minimalist infographics.
Malvika: Thank you Amoolya! That brings us to the end of this conversation. On behalf of our team at INSAID, we wish you good luck for your future!