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, Prem Ranjan stands in the spotlight.
Student Name: Prem Ranjan
Batch: GCDAI – February 2019
Total years of experience: 15 years 8 months
Area of expertise: IT solutions in the healthcare industry
Malvika: Hi Prem, before we begin, can you walk me through your professional journey so far?
Prem: Sure. So I started my career in 2003 in the Information Technology industry. I joined Infosys as a Software Engineer soon after I graduated from college. I worked for Infosys for a couple of years in India. I was based out of their US office from 2005 till 2007. After 2007, I joined my current employer – UST Global and worked with them in the US till 2018. I came back to India after 2018, and am currently based out of Bangalore for the same company.
So far, I have an experience of approximately 15 years and 8 months; playing roles across different levels in an organization in the IT department. So I started as a Software Engineer, I went on to take up business analysis, then Project Manager, Delivery Manager, Account Manager and then moved into sales where I dealt with business development. My main focus is to run the P&L, manage the client relationship, and help generate more revenues for the company.
Malvika: A lot of diversification in your roles! Can you tell me what got you interested in data science and machine learning?
Prem: My goal towards learning data science and artificial intelligence was to adapt to the ever-changing technology landscape. I can say from my experience before 2003, it was all about Java mainframe java.net. So technology changes were there but it was not so rapid; every 3-4 years, there would be a new language, or a new platform or a new product to use. Now, with all the hype around digital transformation, you see so many things changing every month, or even daily.
Primarily, I was looking at something which would help me adapt to this fast-changing landscape, and also learn or make myself stronger on a skill set that is bound to be here for the foreseeable future. And that’s what my thought is- data science is something which will be around for several years from now. It is just a base of something that you can learn, but you can implement it in so many different ways and that will be helpful to every field in the future.
Secondly, as I said that I’m already in the 16th year of my job, I’m looking at leadership roles within my organization. I already reported to my CxOs for a year when I was in the US. Now back in India, I’m still reporting to vertical heads, which are like two or three-level down the leadership team so my focus is to grow in the organization and take up senior positions. I think this will help me position myself better.
If I get my basics right that will help me present my case better to the leadership team, rather than just going around attending conferences or listening to videos or learning through online training. My goal was to get some structured training, which will help me go step by step to learn the basic concepts from scratch.
Malvika: Prem, what is the goal of Data Science?
Prem: Data science is a core concept within this digital transformation landscape.
Largely, what industries are focusing on is to improve each and every touchpoint of customer interaction. It can be your mobile user interface, it can be a transaction, general ease-of-use in apps like PhonePe and all those other apps which have made digital transactions so easy that even people with no knowledge about technology can learn in a heartbeat.
It hardly takes any time for anybody to learn anything that are made so intuitive. Having said that, the primary goal of data science is to analyze the petabytes-gigabytes of data that we are collecting on a daily basis from different touchpoints or platforms and extract some business intelligence or what we call inferences or insights which are finally converted into some kind of data product which will bring business value. So, if there is no business value in return of all the data science that you are doing, it is practically of no use and not something organizations will be looking into.
Malvika: That’s a very valuable bird eye view of Data Science. Are there any current trends in this space that are catching your attention right now?
Prem: Malvika, so far I’ve worked in the healthcare industry and I will base my answer on the same. I was supporting projects for healthcare in the IT space. If I look at the application of artificial intelligence, particularly in the healthcare industry, a few things that pop up are how you can become more descriptive in nature when you are trying to predict customer behavior or customer churn.
You predict how often a person will turn up at a doctor’s office, what can be done to more proactively tell them what needs to be done, so that they don’t have to turn up at a doctor’s office and they can take care of themselves. Its called wearables. We have so many wearables in the market today, which monitor everything from your heartbeat to blood pressure to body temperature and whatnot.
At this stage, in my opinion, all this data is not fully utilized to generate maximum business value. Also currently, we are more into the data collection mode than data analysis mode, so the real business value or the product that we are trying to get out of it, is still not there. That is one of the things that will definitely impact the healthcare industry where more and more focus will go into proactively intervening with the customer to see what can be done to improve their general health.
The second is to address the problem of labor shortage everywhere. We see doctors and services are not available in the remote parts of different countries. So one of the trends of data science we’re building towards, would be to understand what kind of labor optimization you have to do, what kind of labor forecasting you have to do and based on that you will take a call on your hiring decisions- like how many doctors and nurses we need where. Actually, that is something that everyone is struggling with, not only the healthcare or IT industry.
Third will be augmentation of doctors, how can data science and AI augment doctors? This would be like the application of robotics and bots in today’s world. There are so many studies going on where they are trying to build robots which will help doctors in conducting surgery or doing the first round of diagnosis based on data. So the kind of data you’re feeding the robot, they will learn through it and then can do the first level of screening.
The fourth is how we can automate some of the disease diagnosis like breast cancer, brain tumor or bone-related issues. Currently, all the imaging we do is interpreted by the doctors. But with AI and data science and definitely Computer Vision, you can look at all the mammograms, radiograms are electrocardiograms by means of machines. Questions like what should be the case, what are the key insights from the images, etc. and before you go to a doctor, you have these initial insights you can convey to the doctor to make your hospital trips more beneficial and less time-consuming.
Of course, there are all sorts of products that are being generated and have my attention like wearables or protective glasses. The applications are inevitable and endless but from a very broad and high-level understanding that I have today, these are four or five areas that I think AI will be most instrumental in.
Malvika: At INSAID, students are encouraged to build high-quality GitHub profiles. Have
you built a GitHub portfolio and how do you think this will help you?
Prem: Even before I took up Data Science, given my background and role and that I have a team I work with, I used to review GitHub for different kinds of codes and sources. Personally, I hadn’t built anything at that time but I had a fair idea where GitHub can be used; to store any kind of files, use any kind of open software like Java, C, C++.
For now, when it comes to what benefit we get out of GitHub, in my mind I know that data is a platform of the future, more and more people are moving on to GitHub. It is one of the key platforms where you can collaborate with technologists all across the world, to build something.
The beauty of GitHub is that you can source someone’s folder, someone’s code, which you think you can leverage, and you can build on top of it. If you do end up building something substantial, you can get back to the owner to merge, which also helps them. In this way, rather than just taking from the ecosystem, you are trying to give something back to the ecosystem, which is the true power of GitHub.
It helps you learn new stuff, collaborate with people and showcase what you are doing, simultaneously contributing to the whole open-source system ecosystem that we have.
When it comes to my activity on GitHub, since I joined INSAID and had sessions with Nikhil, Manav and Suchit, I built on GitHub profile, I uploaded my EDA analysis and one of the machine learning projects, so I’m trying to be active. At this time, though I’m an amateur, I still think I’ve got the hang of it.
Malvika: 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 a part of Data Science Career Launchpad?
Prem: Yes, yes. So I did have a particular session with INSAID that brought some structure to the mess. We all have a resume, we build a resume, and we update it every six months, one year, sometimes after a year. One of the key things that was told in that boot camp was to make sure you update your resume as frequently as possible at least once in six months.
So what it does, subconsciously, is forces you to think about the last six months, analyze yourself, and answer ‘‘what did I do?” What did I learn in those six months, which will become an important tool for me to set goals for the next six months.
It gives you a structure and some of the best practices that you should follow to highlight stuff on your resume. Go through the process of building the resumes which are very specific for the position that you are applying for. Unless you do that customization, the chances for you getting shortlisted are minimal and that’s very logical but still some people go by a trial and error method which is both time consuming and frustrating.
For somebody like me who looks at resumes every day, I think this was a very good session to at least level a structure. How do you improve your resume, at what frequency you should be applying or updating your resume, deal with what you have done in the last few months and did that really help you grow in your career? Are you on the right track? It’s not until you do that kind of self-introspection, that you’ll get to where you’re trying to go.
Following this kind of exercise helps you focus on your key strengths, where you are lacking and where you should focus. The INSAID session gave me a structured process to build on my niche, an option to become active about my own profile development and learn about both my knowledge and knowledge gap.
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
Prem: Since Data Science itself is kind of fuzzy to people, a lot of them are not clear about it. Sometimes we bracket everything in data science, sometimes we say this is not data science, only exploratory data analysis, then we’ll say something is deep learning and whatnot.
But when it comes to Data Scientists, and it’s distinction from a Data Leader, the split is very, very clear. A data scientist is more of an individual contributor, where their job is to analyze the data in and bring and extract insight out of that data then present their story to the leadership or management or a business/sales team. That’s pretty much the span a data scientist deals with; they might build an algorithm to predict something and do all the fancy technological work that a data scientist will do but that’s where their influence stops.
When I talk about a Data Leader, I’ll split it into data and a leader; leader is a very common term, the job of a leader is not to perform the day-to-day operations, but have a look at the bigger picture.
Data Leaders have to set the vision of the organization or the department they work with, they need to build a road-map and take everyone along towards that goal with influence rather than authority. More importantly they have to be a coach, be an influence on the folks who are working with them; to look at the same vision and march towards it. In addition to that, they are the key people who will take decisions on what business value will the company get from the work that a data scientist has done.
They are the people who have to take care of client relationships, maybe all the P&Ls, revenue to the company and are directly responsible for all that. Briefly, a Data Scientist is a one-person-contributor whereas a Data Leader is a very broad role, where you look at data governance, the status principle, you also look at legal issues sometimes. It would require a lot of cross-functional skill-set and experience, the ability to look at a broader vision, and then tie it together to drive everyone towards a single goal.
Malvika: That was an important distinction made very articulately! Do you have any advice, based on your experience, for a novice in this space?
Prem: Yes sure, I consider myself as a novice in this space as well! In my mind, the first point of clarity is starting small. Don’t go with the crowd. If you start searching on the internet, you will get an overwhelming number of results and advice. Your mind will get cluttered and you will reach nowhere. That was my case, at least the last year, when I did a lot of independent courses. That did help me get a lot of knowledge but none of it was structured and I could not visualize how I was growing step-by-step in the field. So my suggestion for anyone with starting new is to start small, don’t look for headlines claiming you can become a data scientist in 2 months, 11 days or any of those plans, because it will not work out.
If you’re in a job, go through weekend courses. There is no secret sauce to learning data science. Just start small, go through a structured training like INSAID and practice as much as you can.
Malvika: That’s a generous insight! You mentioned some very clear objectives from this course at the beginning of the conversation, would you like to wrap the interview by talking about some ways in which INSAID helped you to work towards them?
Prem: One of the important things that I have found at INSAID is that it is not a start time to end time kind of training, there are no fixed start and end times. Trainers are flexible to spend time to answer questions whenever, even after the session is over.
Secondly, the INSAID academics team is pretty responsive. If you raise a doubt, you get a prompt response. What this does is give someone the confidence that they are working with a team that is there to help them as and when they need!
Another thing is that all the boot camp sessions that we’ve had extend beyond training you on Python, they give you a holistic picture of how to survive in the industry. INSAID does not present you five courses and says you do this and you’ll get a job. It’s not like that and it will never be like that.
INSAID gives you different pointers how you can get engaged with the community, how can you start following influential people, how can you network with those people, how we start following blogs and see what are the trends in it?
INSAID brings together the best practices in the industry for you to get acquainted with and revisit later. This is very concise for someone who is starting afresh start with this course and also wants to explore other stuff.
Lastly, definitely the scholarship. It is an icing on the cake for everyone. So imagine a newbie who is unclear about his goals and objectives with the course will be hesitant to pay for an entire year’s course given that he’s not sure what he will get out of it at the end of the year. INSAID resolves that anxiety with their scholarship program.
That way you can take that first plunge without thinking too much!
So this wholesome approach of INSAID will make their students more holistic rather than someone who’s good at Python. Because, realistically Malvika, there will always be someone who is better at Python than me!
Malvika: Thank you, Prem! This conversation has been riveting. On behalf of our team at INSAID, we wish you good luck for your future!