Data Scientist, AI Consultant, Author
Q1. Please share your educational and professional journey?
My Highest Qualification is M.Tech from BITS PILANI, I have over 7 years of experience in Data Science, Data warehousing, Big Data. I started off my carrier with WIPRO as Data Engineer and transitioned into data science projects spent almost half a decade there, currently working with Infosys as Data Scientist/AI professional/Consultant for almost two years now.
In Infosys, I’ve got ample amount of opportunities to work closely with the customer and solve real-world problems, thanks to my managers Pradeep Kumar K S, Hari Sharma, Sharad Nandini and Ananda Pratim Sengupta for continued support. I was part of the team who recently developed the Data Science framework which received Intellectual property clearance certificate.
Q2. What did attract you towards AI and Data Science Domain?
I personally feel Data Science and AI is a fascinating technology and very beneficial to us and will accelerate mankind’s evolution into the future and I wanted to be an integral part of it.
Q3. What kind of hurdles did you face in your Data Science journey?
Hurdles are part of life but when we have the right people around us it becomes easier, my then manager Animesh Dutta Muhuri, was the one who motivated me to peruse data science in early 2013 to 2014. Back then we didn’t have the right resources unlike now, learning data science was a tedious job.
Q4. Recently you launched your Book “Hands-On Time Series Analysis with Python”. Please share some brief about it and how it is useful for students?
Brief Intro of the book:
The book begins by covering time series fundamentals and its characteristics, Structure & components of time series data, Pre-processing, and ways of crafting the features through data wrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework like StatsModels, pmdarima. Further covers how to leverage advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU, and Autoencoder to solve time series problem using Tensorflow. It finally concludes by explaining the popular framework fbprophet for time series analysis.
This book helps students accelerate understand nitty-gritty details of solving time-dependent problems, most of the structured data with timestamp can be framed into some kind of time series problem and by taking the right approach towards solving the problem we can maximize yield.
Q5. Which kind of Projects do you like most in Data Science Domain?
So far I’ve worked and provided consultation in domains such as Manufacturing, Retail, Wholesale Telecommunications, Brewing, Personal care & Cosmetics, Oil – Gas Production & Transmission. Every domain has its own challenges and problems so nothing in particular.
Q6. Where do you want to see yourself after 10 years in career?
I want to see myself being a Principal Scientist or starting my own firm.
Q7. You are a Data Scientist, AI Consultant and Author. How do you play multiple roles simultaneously in your life?
When we do what we love everything becomes easier, I feel this interest towards work is important helps us to push boundaries.
Q8. What is the role of Python in Machine Learning, Deep Learning and Data Science?
Python had become the go-to language for data science, this demand was born out of the programming language’s versatility. Python is easy to learn has a wide community base and there is a package for almost everything.
Q9. Which is the most difficult part of any Data Science Project?
Explainability of model and results which is steadily evolving.
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