Lab 01
E-commerce A/B Test Analysis
Design, analyse and write up an A/B test on real conversion data with proper power and significance reasoning.
Become a data scientist in 24 weeks.
From statistics and SQL to production ML and deep learning. Kaggle-style hackathons, industry datasets and a portfolio capstone — mentored by working data scientists.
0% EMI availablePlacement guaranteeHands-on labs included
Data Science Track
Only 6 seats left
30-min call · no pressure · get a personal roadmap
24 wks
Cohort length
80+
Labs
9.4/10
Alumni rating
92%
Placement rate
Alumni working at
DNMPramataResponsiveSocial Bytes Pvt LtdUPLDMG
Outcomes
What you'll be able to do on day one at your new job
Every module ends with a graded lab that mirrors what teams actually ship. No slides-only theory.
Frame ambiguous business problems as measurable data questions.
Design and interpret A/B tests, including sample size and power.
Write advanced SQL — window functions, CTEs and query tuning.
Run rigorous EDA and feature engineering on messy datasets.
Train, tune and evaluate classical ML and gradient-boosted models.
Build baseline deep learning models with PyTorch.
Communicate findings via clear dashboards and narrative decks.
Ship a portfolio capstone that stands up to technical interviews.
24-week curriculum
Get the detailed syllabus PDFEight modules. Statistics to production ML.
- Module 01Weeks 1–3
Python + SQL Foundations
The daily tools of the trade.
- Python, Pandas, NumPy
- SQL joins & aggregations
- Window functions & CTEs
- Query optimisation basics
- Module 02Weeks 4–6
Statistics & Probability
The mental model behind every model.
- Descriptive & inferential stats
- Hypothesis testing
- A/B testing & power analysis
- Bayesian intuition
- Module 03Weeks 7–9
EDA & Data Storytelling
Insight before models.
- Structured EDA framework
- Matplotlib & Seaborn
- Plotly dashboards
- Narrative writing
- Module 04Weeks 10–13
Classical Machine Learning
The workhorses of industry ML.
- Regression & classification
- Trees, Random Forest, XGBoost
- Clustering & dimensionality reduction
- Model evaluation deep-dive
- Module 05Weeks 14–16
Feature Engineering & Tuning
Where accuracy actually comes from.
- Encoding & scaling strategies
- Feature selection
- Hyperparameter tuning with Optuna
- Imbalanced data techniques
- Module 06Weeks 17–19
Deep Learning Essentials
Neural networks for tabular, text and image.
- PyTorch fundamentals
- MLPs, CNNs, RNNs overview
- Transfer learning
- Intro to Transformers
- Module 07Weeks 20–22
MLOps & Deployment
From notebook to production.
- MLflow tracking
- FastAPI + Docker serving
- Batch vs realtime inference
- Monitoring & drift
- Module 08Weeks 23–24
Capstone & Interview Prep
Portfolio and hiring readiness.
- End-to-end capstone
- Case study frameworks
- Mock interviews
- Resume + LinkedIn review
Mandatory capstone labs
Projects you'll actually ship
Lab 02
Fraud Detection Model
Handle severely imbalanced transaction data, engineer features, and ship a model with precision/recall trade-offs a business would accept.
Lab 03
End-to-End ML Product
Full pipeline: data ingestion, model training with MLflow, FastAPI serving, Docker deployment and a monitoring dashboard.
1,500+
DS alumni placed
150+
Hiring partners
8 yrs
Training experience
12
Max cohort size
Student stories
Real placements. Real hikes. Real portfolios.
"The A/B testing module was the sharpest I've seen. It's what got me through the Flipkart interview loop."
A
Aditi Rao
Data Scientist · e-commerce
"Going from analyst to data scientist felt impossible until this course. The math + code combo worked."
R
Rahul Menon
Analyst → DS
"My capstone became a talking point in every single interview. It shows I can ship, not just train."
S
Sneha Iyer
Data Scientist
"Weekly mentor reviews turned my messy notebooks into portfolio-quality work."
V
Vivek Anand
Junior DS
FAQ
Everything parents & partners ask
Do I need a math background?
Comfort with basic algebra and probability is enough. We do a 3-week statistics module that ramps up carefully.
How is this different from the AI-ML track?
Data Science is broader — statistics, SQL, storytelling and product analytics alongside ML. AI-ML goes deeper on modelling techniques only.
What roles do alumni get placed into?
Data Scientist, Applied Scientist, Analytics Scientist and Product Data Scientist roles — typically ₹10-28 LPA depending on experience.
Is it online or in-person?
Hybrid. Live weekday classes online, weekend labs and mock interviews at our Chandra Layout campus in Bengaluru (or fully online).
Is there placement support?
Yes — resume rewrites, 3 mock interviews, portfolio reviews and referrals to 150+ hiring partners.
Are EMIs available?
Yes — 0% interest EMIs across 6, 9 and 12 months. Scholarships for women returnees and tier-3 students.
Next cohort · Aug intake
Your Data Engineering career starts with a 30-min call.
Talk to a mentor. Get an honest read on your background, a personalized roadmap, and full fee + EMI details. No pressure.