Your DS Story S01E09: Aman Kapoor (EY)
‘Your DS Story’ is my attempt to bridge the gap between data science professionals & data science aspirants. Here new crop of data scientists will share their experiences, struggles, achievements & their advice so that data science aspirants/enthusiasts can learn and get inspired.
Aman Kapoor is an experienced Data scientist with a short but amazing history of utilizing his concepts in the industry as well as the competitive forums. He has got all this along with his Masters in Data Science from Aegis School of Data Science.
1. Please tell us a bit about your background?
First of all I would like to mention that I am highly obliged to have this opportunity to share my journey in data science and analytics.
Talking about my education background, I am a B.Tech graduate in Biotechnology. As niche we know this field is in India, I shifted my focus to rather something my excited my i.e Mathematics. From my interest in math, logic and business and some research, I came across the field of data science in 2015.
Finally I joined a Post graduate Programme in Data Science for Aegis School of Data Science in Mumbai.
During my masters I studied everything from basic coding to Statistics to Machine Learning & Predictive Modelling
For my strive to learn more and more in this field, I started working on Kaggle and Analytics Vidhya problems during masters itself. Also I worked as a freelancer through Upwork to start understanding the industry requirements from a data scientist.
My first professional experience as a Data Scientist began with Paisabazaar.com where I worked on various projects including basic data mining, predictive modelling and even visualization dashboards. Also I gained some experience with the implementation of predictive models by collaborating with technology teams (something which we miss in the theoretical knowledge).
Then I moved to Ernst & Young as a Consultant. Here I am working with the fraud investigation department which is in the phase of setting up its analytics team. Though here I am not implementing much of advance algorithms/data science but being involved in the ground level of analytics team formation helped me understand the exposure that any industry professional has to analytics.
Currently I am working on providing end-to-end financial fraud detection solution to a client.
What excited me about data science is the immense opportunities we have with multiple problems and solutions using the data especially in India where we are looking at the huge shift in terms of data usage. The popularity of data science is not limited to any industry and that is why this field doesn’t make you domain specific.
2. What projects you are working these days?
These days I am working on a solution to provide end-to-end fraud detection solution to clients for financial frauds across various industries.
The data we get is directly from the client. So the main thing that comes here is to maintain the data confidentiality.We are literally disposed to the entire data of the client.
The main challenge to work in analytics field is that you cannot have a favorite tool/technology. There are times when we need to shift from being a data scientist to a developer who is writing codes to get the files downloaded in right format. Also, you will have to showcase the output to the client in a way that they understand it like a lay-man yet it should have some technical concept involved.
The problems that we get usually involve a person or company at our disposal because our insights shape the next action of the client. So it becomes very important that we are sure of what we are presenting.
Apart from this I have always been active on the data science community so as to never leave touch with this field.
3. How your day to day job looks like?
I have experienced this in all the project I have ever worked on, as a Data Scientist you job is to work on the data. My job generally involves giving
50–60% of my time data reshaping which will include cleaning, formatting, feature generation etc.,
20% of the time to look at the present problem, identify it, prove all our hypothesis (which would also be called as data summarisation),
10% in understanding business and clients mind and
10% to look for a solution which can solve this problem permanently.
4. How you started with DS or transitioned into DS?
As I mentioned earlier, I looked out for a field which will keep me closed to maths. Also I always had an interest in business and not in a same job day in day out.
These factors attracted me to data science.
I first started doing some self paced courses from Edureka to know this domain more and then when I interest increased, I went for my Masters.
It was very important for me to go beyond the classroom learning and that how Analytics Vidhya and Kaggle has helped me lot.These platforms have given me:
data to work on,
forums to read and learn from,
competitive environment to keep the interest high and
now some name because of a little good I have performed there.
Some freelancing projects before joining the actual industry has helped me understand the people’s mindset to data science and analytics.
I am sure I will keep following the same approach throughout my learning period.
5. What advice would you like to give to DS starters or DS transitioners?
There are a several points I would like to mention to the starters in DS:
1. Do not skip the basics. Try to understand statistics and not cram it as it is the foundation.
2. Implement whatever concepts you learn. Practice will only make you perfect. Use open source data to practice more and more
3. Read a lot apart from classroom learning. There are many open source materials available to help you sharpen your concepts here
4. Understand the requirement of data science.
In the Industry:
1. Do not expect only ML in the industry
2. Do not hesitate to do some data exploration
3. Focus on giving business solutions using the data
4. Do not always try to quantify your deliveries in terms for short term money.
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