As an MBA student focusing on Business Analytics, I’ve spent a lot of time learning about how data can shape smarter decisions. I’ve seen how even a small piece of analysis — done right — can lead to better decisions, smoother processes, and fewer surprises.
But like many people in similar programs, I faced a common challenge: how to move from theory into hands-on application — especially without a deep background in programming.
That started to change when I discovered I didn’t need to write complex code.
This article is about my journey into analytics and AI and how it took me to Berlin to compete in an international data science competition, together with my fellow student, Vibha Srinivasa.
Why did you choose visual workflows for data science?
My professor introduced us to KNIME Analytics Platform as an alternative to traditional coding for machine learning. I found the drag-and-drop interface more intuitive and efficient. It allowed me to complete projects much faster than, let’s say, Python.
In a previous credit rating analysis project at university, I had to predict whether customers would default on their loans. We had to do the analysis in Python. It took me the whole semester because first I had to learn Python from scratch.
With KNIME, I was already building workflows after the first class. The fact that KNIME is so instinctive to use made me want to learn more. For example, we learned how to apply the association rule to optimize shelf placement in stores. You identify frequently purchased items and then place them near each other on the shelves. So, how Walmart should be stacking the beer next to diapers! It was really exciting to see how complex things can be done so easily.
How did you make time for learning?
When you like something, you make time for it. I like KNIME a lot, so I make time for it! When you’re building workflows, you’re turning raw data into something actionable and useful. You’re connecting the dots between what’s happening and why it’s happening.
As you build out your workflow, it’s like putting together a puzzle by drag and drop. When it’s finished and you see everything, that makes me feel proud.
Tell us about any projects you’ve completed using KNIME
I did some personal projects outside of class to practice my data science skills and I took part in the Game of Nodes competition: KNIME’s international data science tournament.
Analyzing international students’ wellbeing
In this personal project, I analyzed DataCamp data on international students and mental health. Many international students are very much on their own when they start university. I wanted to see what percentage of students face mental health issues.
I started the analysis in KNIME, and used K-AI, KNIME’s AI Assistant. I could chat with it to ask questions but also get it to build parts of the workflow. It took a bit of time for me to work out how to do the analysis. But I did it. And discovered that 65% of international students face mental health issues.
Competing against data science teams in the finals on stage
My journey learning data science with KNIME led me all the way to the KNIME Game of Nodes Finals in Berlin! This is a tournament where teams compete against each other to solve data analytics and AI challenges.
Teamed-up with Vibbha Srinivasa, we solved a data challenge on stage in front of 300 people.
While we faced experienced competitors, the real victory was the journey itself. We’d gone from being beginners at KNIME to reaching the finals in just six months. I believe this achievement will inspire more students to participate in the future, opening doors to incredible learning experiences and opportunities.
How do you want to put your new skills to use?
I want to move into finance or sales and use my data science skills to inform decision-making.
In the business analytics field, I’ve always wanted to be a manager. But there are already lots of experience-based managers. How can I be different — with my skills?
Every decision today depends on data. Without data, people are making decisions based on experience, but that’s not accurate. With data, even college students can make decisions. Me at 28 or a manager at 45? I can make a better decision if I have the data.
Anything you wish you’d known before you started learning KNIME?
I just wish I’d known KNIME before. It’s like KNIME was built for people like me who want to analyze data with modern techniques but don’t have a background in coding. Ninety percent of my fellow students didn’t have any type of analytics or data science background and everyone liked it. And we got good grades!