Turn Raw Data
Into Real Insights.
Get Hired.
Not Kaggle notebooks in isolation. You'll build end-to-end ML pipelines on real business datasets, create Power BI dashboards for actual clients, deploy models to production APIs, and build a portfolio of data projects companies hire from.
The 90-Day Data Science Roadmap
From Python basics to deploying production ML models and building executive dashboards — mentor-guided every step of the way.
Real Datasets.
Real Business Problems.
You won't be solving toy datasets. You'll analyse real client business data, build ML models that improve actual KPIs, create Power BI dashboards that executives present, and deploy prediction APIs — the full data science workflow from raw CSV to production.
Skills You'll Actually Master
Hands-on proficiency with every tool in the modern data science stack — skills that show up in your GitHub notebooks and Power BI portfolios.
What You'll Analyse & Build
13 weeks of real data science work — every module ends with a notebook or dashboard pushed to GitHub and reviewed by a senior data scientist.
Real Data Projects You'll Actually Build
Real business datasets, real ML models, real dashboards — the kind of portfolio that makes data hiring managers stop scrolling.
Your Data Science Journey
From your first Pandas DataFrame to a deployed ML API and executive Power BI dashboard — every week ends with a real deliverable.
We Don't Stop Till
You're Hired in Data
Data Science roles are the fastest-growing in India — but they demand real project portfolios, not just certificates. Our placement team connects your project portfolio directly with analytics teams, consulting firms, and product companies hiring now.
From Data Beginner to Data Scientist
Real DS alumni. Real models they built. Real offers that launched their data careers.
The Accenture Analytics interviewer asked me to walk through an ML project. I opened my GitHub, explained the feature engineering decisions, showed the SHAP explainability charts, and walked through the business impact calculation. They said it was the most business-aware technical presentation they'd seen from a fresher.
The Power BI dashboard I built during the internship was for a real retail client. When Deloitte asked me to show BI work in my interview, I pulled up a live dashboard with DAX measures and drill-throughs I'd built for an actual business. That's a completely different conversation from showing a Tableau tutorial screenshot.
I switched from a back-office finance job to data science at 26. The Gen AI project I built — a RAG chatbot that queries company sales data in natural language — was what got me the TCS iON analytics role. Nobody else in that hiring cycle had a deployed LangChain application they'd built themselves.
MLflow tracking was the detail that surprised Mu Sigma most. I showed them 47 experiment runs, explained why I chose XGBoost over Random Forest for that specific dataset, and backed every decision with logged metrics. They said it showed maturity they usually only see in candidates with 2 years of experience.
The medical image classifier I built with ResNet50 and Grad-CAM became the centrepiece of my portfolio. Fractal Analytics' technical panel spent 20 minutes asking me about the model architecture choices, the training strategy, and how I handled class imbalance. It was the most enjoyable interview I've ever had.
The SQL case study round at Amazon was the one I was most scared of. But I'd spent 4 weeks writing advanced SQL on real datasets during the internship — window functions, CTEs, complex aggregations. I finished the test in 35 minutes and they gave me 60. Got the offer two days later.
Your first ML model.
Your first Power BI dashboard.
Your first data offer.
Applications open for the next Data Science batch — only 12 seats. Free to apply. We'll call you in 30 minutes.
Free to apply. Merit-based selection. No spam, ever.