Welcome to our podcast series where we share real stories of success from students who have completed our Data and Business Specialization Program. These inspiring conversations feature candidates who got placed through our platform and are now stepping into the corporate world. In today’s episode, we have Madhulita, a final-year B.Tech student from Gokhale College, who recently got placed at Accenture as a Software Engineer. Hosted by Rakshit Vig, mentor of our data program, this podcast covers her journey, skill-building, project highlights, and placement experience.
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Podcaster:
Hi Madhulita, I’m Rakshit Vig, mentor of the data program. Let’s start the podcast with your introduction.
Madhulita:
Sure Sir. I’m Madhulita. I’m an undergraduate B.Tech student at Gokhale College, currently in my final (fourth) year. I’m also working on my capstone project right now. Recently, I’ve been placed at Accenture as a Software Engineer.
Podcaster:
That’s great! And your specialization is in which branch?
Madhulita:
Data Science.
Podcaster:
So you’re doing a B.Tech in CSE with a specialization in Data Science, right?
Madhulita:
Yes Sir, that’s correct. It’s a new specialization introduced in our college.
Podcaster:
Nice. Now tell me a bit about your technical skills. What do you consider your core strengths?
Madhulita:
My core skills are focused on data structures and problem-solving—something we regularly practice through coding sessions in college. We also had aptitude training to prepare for interviews. Alongside that, I’ve worked on improving communication skills.
I’ve done internships like one at Juniper Networks, where I explored networking. I also worked on data visualization using Power BI, SQL databases, and version control through GitHub. These are the skills I’ve built myself through personal effort.
Podcaster:
That’s impressive! So, tell us about the interview process—how many rounds were there and what type of questions were asked?
Madhulita:
There were four rounds. First, we had an aptitude round. Then, a coding round with two questions. After that came a communication test—it wasn’t technically an elimination round, but still, some candidates were screened out. Finally, there was the main interview.
Podcaster:
Got it. And for the coding round, which programming language did you choose?
Madhulita:
We were allowed to choose between Java and Python. I opted for Java, as we’re from the Java stream.
Podcaster:
Understood. Now tell me about your key projects—what kind of academic or personal projects do you have in your resume?
Madhulita:
In my third year, I completed a Machine Learning project on customer segmentation using K-means clustering. It focused on analyzing customer credit details. This project helped me a lot during the placement process.
I come from a diploma background, and during that time I also worked on an AI-based attendance journal system using computer vision for our college. These were the two main projects I discussed during interviews.
Podcaster:
Those are solid projects! And now in your final semester, what’s your current capstone project about?
Madhulita:
I’m working on a flight delay forecasting system using federated learning. I collected real-time data from the meteorological department by sending them a formal request via email.
Podcaster:
That’s excellent initiative. Reaching out for real data really shows your commitment.
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Don’t miss the full conversation—watch the podcast now and get inspired by Madhulita’s journey!
General interview questions answered by Madhulita during her selection process
What was your role in the customer segmentation project using K-means?
Sample Answer: I led the data preprocessing and algorithm implementation phase. I used Python with scikit-learn to clean the dataset and apply K-means clustering. The goal was to group customers based on credit details to help businesses make better marketing decisions. I evaluated the model using silhouette score and visualized clusters with matplotlib.
Which programming languages are you proficient in, and which one do you prefer for coding interviews?
Sample Answer: I’m proficient in Java and Python. I prefer Java for coding interviews because that’s what I practiced most in my curriculum and during competitive coding sessions. However, for data analysis and machine learning, I usually prefer Python due to its vast library support.
Explain the concept of federated learning in your capstone project.
Sample Answer: Federated learning is a decentralized machine learning technique where the model is trained across multiple devices or servers holding local data, without transferring raw data to a central server. In my project, I used it to forecast flight delays using real-time meteorological data, ensuring data privacy while maintaining accuracy.
What is the difference between supervised and unsupervised learning?
Sample Answer: Supervised learning involves training a model on labeled data, where both input and output are known. Examples include regression and classification tasks. Unsupervised learning deals with unlabeled data where the model tries to find hidden patterns or groupings—like clustering using K-means, which I implemented in my project.
How would you explain Power BI to a non-technical stakeholder?
Sample Answer: Power BI is a tool that helps convert raw data into interactive dashboards and reports. It allows business users to visualize trends, monitor KPIs, and make decisions based on data—without needing deep technical knowledge.
What challenges did you face during your internship, and how did you overcome them?
Sample Answer: During my internship at Juniper Networks, I initially struggled with adapting to a new tech stack and real-time data handling. I overcame it by proactively learning through documentation and asking questions during team discussions. This helped me complete my assigned data visualization task confidently.
Can you explain a situation where you used problem-solving skills in a real project?
Sample Answer: In my machine learning project, I encountered missing and inconsistent data that led to inaccurate clustering. I used data imputation techniques and feature scaling to clean and normalize the data. This significantly improved clustering performance and result interpretability.
