A complete tech buff with flair to develop new technologies, Soumyajyoti Bhadra wanted to have an in-depth understanding of data science, artificial intelligence and machine learning. So, to quench his curiosity he enrolled for the Global Certificate in Data Science (GCD) program at INSAID.
In this interview, let’s see what is his take on the impact of data science across industries and how his experience has been at INSAID.
Question 1: Which program & batch are you part of at INSAID & tell us more about your current work profile?
Soumyajyoti: I am part of the Global Certificate in Data Science & AI (GCDAI) program, May 2020 batch. Currently, I am working in TCS as Assistant Consultant.
Question 2: Walk us through your career journey & what got you interested in Data Science & Machine Learning?
Soumyajyoti: I have completed my B.Tech in Computer Engineering from West Bengal University of Technology. Right now, I am pursuing the GCDAI certification from INSAID. I also have my eyes on the Post Graduate Diploma in Data Science from IIT Madras.
During my college days, I had also participated in The Great Mind Challenge (TGMC), an all India contest organized by IBM and was one of the top finalists in the competition.
My interest in Data Science, Artificial Intelligence and Machine Learning grew because of mainly three factors. First, I am a tech buff. I love to read and watch anything on the latest developments of technology, how it was before, etc.
I love developing pretty much anything, may it be hardware or software to simplify any sort of task. It is no less than an adrenaline rush for me as it involves an immense sense of achievement.
One of my favorite past times in fact is CNC project development. It basically involves robotics, Raspberry Pi and other similar technologies.
Secondly, the fact that data science can with the use of historical data automate many of the computational tasks grabbed my attention. No matter what solution you build or develop manually on computers, it is pretty consuming.
And even after trying to make them as efficient as possible through APIs, modules, low code solutions, etc. it still demands your effort and time. But with automation that is not the case and you can use programming languages like Python to do it.
I think that is pretty interesting as I have learned it in college. During that time, I have learnt several programming languages like Perl, C++ and others. And as data science involves both coding and analysis, I thought this is exactly the thing for me.
And the third and final factor that drove me towards data science, artificial intelligence and machine learning is the job that I do which involves issues, bugs and gaps largely impacting production. Now in the production environment you will not receive all the cases. You yourself will have to find out what the actual issue or the pattern of the issue is.
But I really enjoy finding out patterns. Users might complain about two or three instances but then I will have to find out the pattern from those two instances to create the hypothesis of what might be the actual issue.
I have to go through databases to find out the patterns and check if they are true to my hypothesis or not. But with the power of data science skills I won’t have to take so much effort as then everything will be well analyzed and identified.
Question 3: What all tools and packages in Data Science & Machine Learning have you mastered in your Data Science & AI program at INSAID so far?
Soumyajyoti: I have mastered Python along with NumPy and Pandas packages. And apart from that, in Machine Learning Intermediate, I am working on Scikit-Learn. Also I have gone through the inner workings of Jupyter Notebook.
For instance, how to set up the server, how to start up Jupiter server from the command line, how it interacts with the Python environment, what are the options, etc.
Incidentally, I also bought a new laptop with a very powerful Nvidia GPU to master the PI CUDA package so that I can use GPU assisted computing in the future.
Question 4: What were some of the initial challenges when you got started on your Data Science journey and how did you overcome it?
Soumyajyoti: The first and foremost challenge in learning data science, artificial intelligence and machine learning was to unlearn to learn new things. It was really tough to unlearn all those that I have known since college and things I have known about data science prior to INSAID.
I had to brush up my statistics, because I’ve not used that statistics knowledge for a long time. And as far as I think, unless you understand the basics of statistics, it’s very hard to go and do anything in data science. It’s not only about coding, it’s about understanding what specific thing needs to be applied.
And I, personally, have a drawback. I am not satisfied by my learning, or I don’t feel confident on the subject until unless I go very deep, and at least get to learn the simple basics of what I’m trying to learn.
And here, of course, it’s a two days class on weekends. So keeping in mind the vastness of data science, artificial intelligence and machine learning, the classes are very compact and scheduled. But whatever was taught, from the basics point of view, is generally taught at a very high level. I had a problem in understanding the basics.
So I took out all my college books, studied them and did some ground work. I also went through some books online. So, that pretty much cleared the confusion and doubts. And I overcame the challenge of unlearning by going through the class notes and filtering whatever I heard before out of my mind.
Question 5: Who is your favorite faculty at INSAID and what did you learn from him the Most?
Soumyajyoti: I had the privilege of listening and learning from Suchit as well as Deepesh. Each one of them has really taught some invaluable lessons all together because their teaching style is different.
I really like the teaching style of Deepesh. The way he goes into the basics, and then comes back is really good. It helps us keep in mind the preliminary lessons.
And the lesson that I’ve learned from him is to take baby steps, because we as students just starting off with data science cannot reach the level of expertise of the professionals who have done PhDs and are working in this field for a long time. So it’s important to keep on practicing.
Question 6: What is the goal of Data Science?
Soumyajyoti: For me personally, the goal of data science is to make lives easier by solving problems, providing direction to decisions and offering us insights. It also helps in simplifying processes, and making them more efficient.
One of the best examples of this can be the invention and production of COVID vaccines. As far as I remember the last time, it took 40 years to create a vaccine. But this time we minimized it to even less than a year. That’s the power of data science, artificial intelligence and machine learning.
Question 7: In your view, how has Data Science evolved in the last few years?
Soumyajyoti: Data Science has evolved in such a way that it has proliferated our lives. Five or six years back, you could not have imagined that you would have a device through which you can order groceries, but right now you have them at the palm of your hands.
Currently, it’s common to come across CML or power devices, which I think may be marked as artificial intelligence and machine learning powered devices, but data science is the core of it. Because otherwise, if you don’t combine your domain statistics and computing, then you don’t have those devices.
Although data science has always been there through data sheets on Excel, etc., manual effort was required to analyze them. And, compared to the data production in those times, right now it’s a billion times more and beyond the human capability of managing it. That’s why Big Data computation setups are required and cloud computing is all the rage right now.
But just as positive trends, I would also like to point out the negative trends of data science or the ill use of data science. The gradual rise in the autonomy of machines can distress people as it will replace human jobs.
Furthermore, examples like discrepancies in election data and the development of autonomous war machines are some advancements that worry us rather than making us happy.
Question 8: Which are some of the blogs that you follow?
Question 9: What is your advice to anyone wanting to start a career in Data Science?
Soumyajyoti: Clear your basics in data science, artificial intelligence and machine learning. Don’t get bogged down by the fact that you have no knowledge in coding. You are going to learn something that it’s very tough. Don’t go by hearsay knowledge or decisions. Stick to the basics, take baby steps and keep on practicing. That should take care of the entire thing.
This was a conversation with one of our GCDAI students – Soumyajyoti Bhadra.
If you want to read more interesting student interviews, visit INSAID Spotlight.