← All programmesUnderstand the data.
DATA SCIENCE / CORE PROGRAMME
Understand the data.
Build the model.
Data Science
Move from analysis into machine learning, deep learning and deployment. Learn the decisions behind a model, then turn your work into an application people can try.
For analysts, graduates and domain experts moving into ML
THE MODEL WORKSPACEEXPLORE ↓
Understand before predicting.
Inspect the data, define the target and build features that reflect the question you are trying to answer.
YOU BUILDAnalyst-stack portfolio
Illustrative learning workflow · Select a stage to explore
6 months
Programme duration
8
Curriculum modules
Live
Practitioner-led learning
Build
Projects & capstone
A STARTING POINT THAT FITS YOU
Bring your curiosity. Build your capability.
From data foundations to machine learning, deep learning and deployment.
DA/BA alumni on the fast-track
Plan my starting point ↗Domain experts (pharma/BFSI/retail) adding ML to their toolkit
Plan my starting point ↗THE CAPABILITIES YOU DEVELOP
Machine learning end-to-endDeep learning foundationsNLP & text analyticsModel deploymentTransformer fundamentalsGenAI-augmented analysis
YOUR LEARNING JOURNEY
Learn the model. Own the whole pipeline.
Move through the analyst core, machine and deep learning, NLP and transformers, then deployment and a capstone defence.
MODULE 01 / 0840 live hours
Data Analytics Core (condensed)
WHAT YOU WILL EXPLORE- Excel/SQL/Power BI + Python + EDA + statistics — fast-track for DA alumni
THE WORK YOU BUILD
Analyst-stack portfolio
MODULE 02 / 0832 live hours
Machine Learning
WHAT YOU WILL EXPLORE- Feature engineering
- trees, random forest, XGBoost
- clustering
- SVM
- validation, tuning, imbalanced data
THE WORK YOU BUILD
Churn + segmentation + fraud projects
MODULE 03 / 0816 live hours
Deep Learning
WHAT YOU WILL EXPLORE- ANN, CNN, RNN
- Keras/TensorFlow or PyTorch
- transfer learning intro
THE WORK YOU BUILD
Image or sequence model project
MODULE 04 / 0812 live hours
NLP & Text Analytics
WHAT YOU WILL EXPLORE- Tokenization, TF-IDF, embeddings, sentiment analysis, classification
THE WORK YOU BUILD
Review-mining project
MODULE 05 / 086 live hours
Time Series (elective)
WHAT YOU WILL EXPLORE- Decomposition, smoothing, ARIMA, accuracy metrics
THE WORK YOU BUILD
Sales forecasting mini-project
MODULE 06 / 0814 live hours
GenAI & Transformers for Data Scientists
WHAT YOU WILL EXPLORE- Prompting for DS work
- transformer architecture & attention (conceptual + Hugging Face hands-on)
- embeddings
- RAG intro
THE WORK YOU BUILD
GenAI-augmented analysis + transformer notebook
MODULE 07 / 0810 live hours
Model Deployment
WHAT YOU WILL EXPLORE- Flask/FastAPI services, Streamlit apps, Docker intro, monitoring basics
THE WORK YOU BUILD
Deployed ML model with API
MODULE 08 / 0812 live hours
Industry Capstone
WHAT YOU WILL EXPLORE- End-to-end ML product: business problem → model → deploy → present
THE WORK YOU BUILD
Capstone + demo day
MAKE ROOM FOR THE PRACTICE
Live learning, eLearning and independent study across six months. Explore the individual module allocations above and discuss the weekly timetable with a counsellor.
Discuss the schedule →YOUR WORKING TOOLKIT
PythonSQLPower BIscikit-learnXGBoostTensorFlow/KerasPyTorchNLTKspaCyHugging FaceFastAPIStreamlitDocker
BUILD AS YOU LEARN
A portfolio with a story behind every piece.
Explore the work attached to each module. Follow the progression from your first exercise to the final capstone.
PROJECT MILESTONE 01
Data Analytics Core (condensed)
Analyst-stack portfolio
CURRICULUM PROJECT / LEARNING ARTEFACT- 01Understand
- 02Build
- 03Review
- 04Explain
CONNECT THE LEARNING TO THE WORK
Excel/SQL/Power BI + Python + EDA + statistics — fast-track for DA alumni
MAKE YOUR WORK EASY TO DISCUSS
What question did you start with? What did you build? Explain one decision, one limitation and what you would improve next.
YOUR PROGRAMME PORTFOLIO
- Churn, fraud & segmentation models
- Image/sequence deep-learning project
- NLP review-mining project
- Sales forecasting
- Deployed ML model with API + UI
- Capstone with viva
WHAT THE WORK ADDS UP TO
Learn it. Build it. Explain it.
Prepare the foundation
Build on Python, EDA and statistics before moving into modelling.
Compare approaches
Work with machine learning, deep learning, text and transformer fundamentals.
Evaluate thoughtfully
Practise validation, tuning and metrics appropriate to the modelling task.
Deliver an application
Deploy a model behind an API and present the end-to-end capstone.
CAPABILITY DIRECTIONS
Data ScientistML Engineer (entry)Senior Data AnalystAI Analyst
These roles describe directions the curriculum supports. They are not a guaranteed job title, employer or salary.
PLAN YOUR STARTING POINT
Turn your interest into a learning plan.
Select what describes you today. Use it to prepare for a conversation about the course, your goals and the time you can commit.
0 / 4
Select the statements that fit. You can still enquire with any number selected.
The programme includes a condensed analyst core. Discuss your starting point with a counsellor; Data Analyst is the foundation-first option if you need more time with the basics.
Instructors
Taught by practitioners, not presenters
Both instructors hold full-time senior data science roles. You are learning from people who ship this work every week.
PK
Pankit Kumar
Lead Instructor
Sr. Data Scientist, Parexel (a Goldman Sachs–backed company)
10 years in Data Science & AI, building and shipping production systems in regulated pharma/clinical environments. Freelance trainer at BIA, AnalytixLabs and Scaler — he has taught this material to thousands of working professionals.
Data ScienceMLOpsClient deliveryTeaching
IS
Ishaan Sharma
Co-Instructor — AI / GenAI
Senior Data Scientist, Nuvo AI (Meril Group)
Close to a decade across NLP, computer vision and Generative AI. Microsoft Azure ML Scholar. Works on production AI systems in medical devices and healthcare, and leads the agentic-AI and MCP modules.
GenAINLP / CVAgents & MCPLLMOps
BEFORE YOU ENROL
A clear picture of your next step.
Understand the starting point, learning format and portfolio before choosing your programme.
Is six months enough to become a data scientist?
For someone with the analyst basics and consistent effort, yes — this is a focused, project-heavy six months that takes you to job-ready. If you're starting from zero data experience, we'd honestly recommend beginning with analytics first; data science builds directly on that foundation.
Does the course cover GenAI, LLMs and deployment, or just classical ML?
Both. Alongside machine learning, deep learning and NLP, there's a dedicated GenAI and transformers module and a full deployment module (FastAPI, Streamlit, Docker). Shipping a deployed model is part of the capstone — it's what makes you stand out in interviews.
What's the fee, and can I pay in instalments?
The fee is inclusive of GST and all charges, with 0% EMI plans letting you pay in up to four instalments. A counsellor shares the figure on your call.
Do I need a maths or computer-science degree?
No specific degree is required. You need working numeracy and logical thinking. Many strong data scientists come from commerce, biology or mechanical backgrounds — consistency and a willingness to build projects predict success far better than your marksheet.
What support is there for getting hired?
Assured job interviews through our hiring network, interview and portfolio prep, and a post-course internship on real problems. The final offer depends on your interview performance; we don't promise a specific job or salary.
Read the full Data Science guide +
What a data science course should actually teach in 2026
A lot of data science courses in India are still teaching 2019's syllabus — train a model in a notebook, plot a confusion matrix, done. The market has moved on. Companies now expect a data scientist to deploy what they build and to understand modern AI: LLMs, transformers, and how GenAI augments analysis. A course that stops at scikit-learn is leaving you behind before you graduate.
This programme is built the way the job is actually done. It runs from the analyst core (condensed, because you need it) through machine learning, deep learning and NLP, into a dedicated module on GenAI and transformers *for data scientists*, and finishes with real model deployment — FastAPI, Streamlit, Docker. You don't just train a churn model; you ship it behind an API with a UI someone can use. That single skill separates you from the majority of fresh data science graduates.
The path through the material
We assume you're coming in with the analyst basics (or we bring you up to speed fast), then build: supervised and unsupervised ML with honest model evaluation, deep learning foundations, NLP and text analytics, an optional time-series track, the modern AI layer, and deployment. The projects are the point — churn, fraud and segmentation models, a deep-learning image or sequence project, an NLP review-mining build, sales forecasting, and a deployed model with a live API. You leave with a capstone and a viva, not a participation certificate.
Who it's for — and who should wait
It fits graduates and analysts with zero to four years of experience moving into ML, our own Data Analyst alumni on the fast track, and domain experts in pharma, BFSI or retail who want to add machine learning to what they already know. If you have never touched data before, be honest with yourself — start with analytics first and grow into this. Data science sits on top of the analyst stack; skipping the foundation is the single most common reason people stall.
Salary and the roles this opens
Data scientist, entry ML engineer, senior data analyst and AI analyst are the direct targets. Data science pay in India spans a wide range — roughly ₹6–12 LPA at entry, ₹12–25 LPA mid-level, and ₹25 LPA and beyond at senior/lead — and the fastest way to climb it is exactly what this course emphasises: deployment and modern AI, not just modelling. We break the numbers down honestly in our guide to data scientist salary in India, or get a personalised estimate with our free data science salary calculator. As always, treat published figures as market signals, not guarantees.
Free resources to explore first
Not ready to commit? Start with our free guides, written by the same instructor who teaches this course: what data science actually is in 2026, the full step-by-step data science roadmap, where to begin with Python and SQL, project ideas that get you hired, and an honest look at whether data science is a good career in India. If you're weighing analyst vs scientist, read Data Scientist vs Data Analyst.
CONTINUE EXPLORING
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Read the guide · 6 min ↗How to Become a Data Scientist in India (2026): A Step-by-Step Roadmap
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Read the guide · 7 min ↗Data Science Roadmap 2026: What to Learn, in What Order
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Read the guide · 11 min ↗YOUR NEXT STEP / DATA SCIENCE
Understand the data.
Build the model.
Get the detailed syllabus and talk through your starting point, weekly schedule and the work you want to build.
6 monthsLive practitioner learningFees shared on a call
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