Srinidhi Govind, a project manager in analytics, has worked with multiple BFSI clients and tackled data like a pro. But he wanted to learn more and equip himself with data science and machine learning skills to escalate his work. So, he enrolled for the Global Certificate in GCDAI program at Accredian.
In this interview, let’s see what he thinks is important for data science aspirants to know before jumping into a formal course and how his learning experience has been at Accredian.
Question 1: Which program & batch are you part of at Accredian & tell us more about your current work profile?
Srinidhi: I am part of the Global Certificate in Data Science & AI (GCDAI) program, March 2020 batch. Currently, I am working as Project Manager, Analytics in EXL.
Question 2: Walk us through your career journey & what got you interested in Data Science & Machine Learning?
Srinidhi: I have been working for BFSI clients for the past two and a half years. My role has overlapped with the roles of a consultant as well as a project manager. As a consultant, my work comprises discussion with the clients about their pain points, and suggest a way in which AI and ML could help them.
For example, let’s say a client has to reduce costs of manual work, and repeated labor, we could suggest them RPA solutions. If a client has to detect fraud transactions, we may suggest anomaly detection. Apart from this, I also pitch clients about our existing products and solutions so that they’re aware of the solutions available at our end.
Coming to my role as a project manager, I get all required client projects executed end-to-end. Before a certain project starts, the task would be to understand the requirements, designing the process, preparing the project roadmap, planning the resources, and providing budget estimations.
Once we get to go ahead with the project, I have to work closely with developers, assist them on the development, ensure timely delivery of the sprints according to plan, regularly update the clients about the progress, starting from user testing to final deployment.
So as I said, my role comprises both the role of a consultant as well as a project manager. Initially, I suggest what kind of solution should the client go ahead with and once we get approval, I get it executed with the help of developers.
Question 3: What all tools and packages in Data Science & Machine Learning have you mastered in your Data Science & AI program at Accredian so far?
Srinidhi: I have majorly worked on the coding language of Python and its various packages like Numpy, Pandas, PyPI. for data processing and calculations, Scikit-Learn for modeling, Matplotlib for data visualization, and Pi Carrot for Auto Ml.
I’m yet to explore other coding languages such as R or SAS, and other tools such as Tableau and Power BI. I have worked on all three major environments of Jupyter, Spyder and Google Collab. And, I’ll be exploring many more packages and tools in the coming days.
Question 4: What were some of the initial challenges when you got started on your Data Science journey and how did you overcome it?
Srinidhi: Two of the major challenges which I faced when I started my data science journey, were one I couldn’t remember what was taught in class, in spite of being attentive in the class and noting down the things.
The only way to overcome this, according to me, is by practice. So I started practicing whatever was taught. Unless we open a notebook, start coding, and try out various things ourselves, we can’t learn and remember them.
My second challenge was, I didn’t know where to get the supplementary information. So what is taught in class might not be entirely enough for everyone. So what I did was I started reading blogs on AnalyticsVidhya and Medium, watched videos on YouTube, attended any other free HTML sessions which were going on, and tried to get the supplementary information and varied opinions.
I also tried to participate in a few of the competitions which AnalyticsVidhya and Kaggle conduct. Even though my performance was not very good, it gave me a good starting point to understand the methodology followed while building a model. Accredian also has a very good set of videos on YouTube, which offers the supplementary information required.
So these things helped me get the supplementary information which I felt I was lacking initially.
Question 5: Who is your favorite faculty at Accredian and what did you learn from him the Most?
Srinidhi: My favorite faculty at Accredian is Neelmani. He taught us term three, which is actually very critical in terms of laying the foundation for the entire machine learning journey and contained the core principles of how we go about building and evaluating a model.
Neelmani had a very good method of teaching, which was initially used to cover the theory, followed by the practicals. But my favorite part, the bonus round started after the class was over and the Q&A sessions actually started to clear every doubt of ours.
He used to give practical examples from his work life like how to explain a model to senior management, deal with a client who doesn’t understand anything about data science, what happens after a Markov model is deployed, does it require constant monitoring, who are some coding advocates, etc. And that made me connect the entire theory into the practical world.
Question 6: Which are some of the blogs that you follow?
Srinidhi: Usually, I follow a few blogs. AnalyticsVidhya is one of them. I also read blogs on Medium, Towards Data Science and Towards AI. These blogs have a lot of articles on every topic, which is written both by new data scientists as well as seasoned ones. So majorly these are the blogs which I follow.
Question 7: What is your advice to anyone wanting to start a career in Data Science?
Srinidhi: I would like to majorly give three pieces of advice to anyone who is wanting to start a career in data science. First, understand if it is truly your passion. Don’t pursue it just because it may sound cool or you just want to do it because others are doing it.
Do some research about data science and what different aspects it deals with. You will have to play a lot around data, analyze them, clean them and understand them. And these things can be boring to a lot of people. Statistics and percentage of coding is also involved in data science. So decide wisely and pursue only that which interests you.
My second advice would be to be cognizant of the fact that this path is not going to be easy. You would have to spend a lot of months learning the concepts and practicing them. You would have to keep yourself updated with the latest trends, follow blogs, and dedicate a lot of time for it.
And whichever course you join, they cannot teach you everything in data science, nor can they spoon feed you the portions entirely. You will have to work hard, so be mentally prepared for it.
The third thing would be if data science is totally new to you, it is best to have guidance in the form of online school or offline school based on your preference. Of course, there are people who have become data science experts by learning directly from the internet.
But I think, if you want to take that the road of self learning, you must have decent coding experience in any language and must have a good maths background and a lot of rigor and commitment.
So decide wisely how you want to learn data science, is it with self help or with the help of courses.
This was a conversation with one of our GCDAI students – Srinidhi Govind.
For more such interesting student interviews, check out Accredian Spotlight.
