Become a job-ready Data Scientist with Gen AI
Master Python, statistics, machine learning and deep learning — then go further and build with generative AI, LLMs and AI agents, backed by real projects and interview coaching that carry you to the offer.
Still deciding? Talk to a mentor first.
A free 15-minute call to map your path to a job-ready Data Scientist with Gen AI — honest guidance, zero pressure.
- Personalised learning roadmap
- Full syllabus, batch dates & fees
- Scholarships & no-cost EMI options
- Placement & interview support
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The data-driven career, now
powered with Gen AI
Every industry — banking, healthcare, retail, logistics — is turning its data into decisions, and now racing to add generative AI on top of it. Pair the classic data science skill set — Python, statistics and machine learning — with the ability to build LLM and agentic applications, and you become the kind of data scientist teams are scrambling to hire.
Data science and AI roles are among the fastest-growing, highest-paid skills in the Indian job market.
Every company is racing to ship LLM and agentic features — people who can actually build them are scarce.
Portfolio projects — including Gen-AI and agentic builds — you can walk an interviewer through end to end.
Python, statistics, machine learning, deep learning and generative AI — the complete modern data skill set.
Built for four kinds
of people
Whatever you’re starting from, the course begins at fundamentals and takes you to job-ready — with the Gen-AI and agentic skills employers now expect.
Students & freshers
Final-year students and recent graduates who want a real data skill and a portfolio before applying.
Non-IT career switchers
Coming from a non-technical background. We start at zero — no coding or statistics experience assumed.
Working professionals
Analysts, developers and support engineers moving into data science and AI roles. Weekend batches available.
Self-taught analysts
You know some Python or Excel but need structure, real projects and someone to review your models properly.
Four phases, twelve modules,
one Gen-AI-ready data scientist
Every module ends in hands-on work. Nothing here is theory-only.
Python & data foundations
Start from programming logic and finish able to write clean Python, reason with statistics and wrangle real datasets — using AI coding assistants the way modern teams do.
Python for data science
Weeks 1–3Programming logic·Data types & control flow·Functions & modules·Data structures·File handling·Jupyter & notebooks·Virtual environments·AI pair-programming (GitHub Copilot)
Statistics & probability
Weeks 3–5Descriptive statistics·Probability·Distributions·Sampling·Hypothesis testing·Correlation & covariance·Confidence intervals·Statistical thinking for models
Data wrangling & EDA
Weeks 5–7NumPy arrays·Pandas dataframes·Cleaning & missing data·Merging & grouping·Feature engineering·Exploratory data analysis·Outlier detection·Working with real datasets
Analytics & machine learning
Turn data into insight and prediction — visualise it, query it at scale with SQL, then build and evaluate real machine-learning models.
Data visualization & storytelling
Weeks 7–9Matplotlib·Seaborn·Plotly·Chart selection·Dashboards·Power BI / Tableau basics·Communicating insight·Data storytelling
SQL for data science
Weeks 9–10RDBMS concepts·SELECT & filtering·Joins & subqueries·Aggregations & grouping·Window functions·CTEs·Query optimisation·Connecting SQL to Python
Machine learning
Weeks 10–13Supervised vs unsupervised·Regression·Classification·Clustering·Decision trees & ensembles·Feature scaling & selection·Model evaluation & cross-validation·scikit-learn
Deep learning & Generative AI
Move from classical models to neural networks and language — then learn how LLMs work and build your first generative-AI applications.
Deep learning & neural networks
Weeks 13–16Neural network basics·Backpropagation·Activation & loss functions·CNNs for images·RNNs & sequences·Transfer learning·TensorFlow & PyTorch·Model training & tuning
Natural language processing (NLP)
Weeks 16–18Text preprocessing·Tokenisation·Word embeddings·Sentiment & classification·Named-entity recognition·Transformers·Hugging Face·Fine-tuning basics
Generative AI & LLMs
Weeks 18–20How LLMs work·Prompt engineering·OpenAI & Gemini APIs·Embeddings·Vector databases·Retrieval-Augmented Generation (RAG)·LangChain·Responsible & safe AI
Agentic AI, production & career
Build autonomous AI agents, ship your models to production, and get interview-ready with a portfolio to show.
Agentic AI
Weeks 20–23Agent architectures·LangChain & LangGraph·Tool-using agents·Memory & planning·Multi-agent systems·Model Context Protocol (MCP)·Evaluation & guardrails·Building agentic apps
MLOps & deployment
Weeks 23–24Git & GitHub·Model packaging·FastAPI & Streamlit·MLflow & experiment tracking·Docker·CI/CD intro·Cloud deployment (AWS)·Monitoring
Gen-AI capstone & interview prep
Weeks 24–26Capstone scoping·End-to-end Gen-AI / agentic build·Portfolio & GitHub·Resume building·DS & ML interview drills·Case studies·Mock interviews·Communication practice
Want the full module-by-module syllabus?
We’ll send the complete curriculum PDF along with batch dates and fee details.
You graduate with a
portfolio, not just notes
Four complete builds — including generative-AI and agentic applications — created the way real teams work: version controlled, evaluated and deployed.
Machine-learning prediction model
An end-to-end ML project — from raw data through cleaning, feature engineering and model selection to an evaluated, tuned predictor (e.g. churn, price or risk).
NLP / LLM application
A language application — sentiment analysis, text classification or an LLM-powered summariser — using transformers and Hugging Face models.
RAG assistant & AI agent
A retrieval-augmented assistant that answers from your own documents, extended into a tool-using AI agent that can act on tasks.
Capstone — your own Gen-AI build
Scope, design and ship a generative-AI or agentic application of your choosing, with mentor guidance and code review at every stage.
The complete
working stack
Python & data
- Python 3
- NumPy
- Pandas
- Jupyter
- Feature engineering
- EDA
Statistics & ML
- Statistics
- Probability
- Hypothesis testing
- scikit-learn
- Regression & classification
- Clustering
Deep learning
- TensorFlow
- PyTorch
- Neural networks
- CNNs & RNNs
- Transfer learning
- NLP
Data & visualization
- SQL
- MySQL
- Matplotlib
- Seaborn
- Plotly
- Power BI / Tableau
AI & Generative AI
- LLMs
- LangChain
- RAG
- Embeddings
- Vector databases
- AI agents
- Hugging Face
- Prompt engineering
MLOps & tools
- Git & GitHub
- MLflow
- FastAPI
- Streamlit
- Docker
- AWS
- Jupyter
Roles you can apply for
when you finish
The same skill set opens several doors — from core data roles to the fast-growing Gen-AI and agentic-AI titles our learners now target.
Data Scientist
Turn data into models and insight, from exploration through to production predictions.
Machine Learning Engineer
Build, tune and deploy ML models and pipelines that run reliably in production.
AI Engineer
Design and ship applications powered by deep learning, LLMs and modern AI tooling.
Generative AI Engineer
Build RAG pipelines, LLM apps and autonomous agents on top of vector stores and frameworks.
NLP Engineer
Work on language models, text understanding and conversational AI systems.
Data Analyst
Query, visualise and communicate data to drive decisions — a strong first step into data science.
Resume & portfolio
We rewrite your resume around your projects and set up a GitHub that reads well to a reviewer.
Interview drills
Statistics, machine learning, Python, SQL and Gen-AI question banks, practised until the answers are automatic.
Mock interviews
Technical and HR rounds with working data scientists, followed by honest feedback.
Hiring introductions
Your profile shared with our hiring-partner network as suitable roles open up.
Pick a batch
that fits your week
EDIT: replace the dates and timings below with your actual batch schedule.
Weekday morning batch
Weekday evening batch
Weekend batch — for working professionals
What every enrolment includes
- Live instructor-led sessions with recordings for revision
- Small batches with dedicated doubt-clearing time
- Four portfolio projects — including Gen-AI and agentic builds — with mentor code review
- Resume building and mock interview rounds
- Placement assistance that continues after your last class
- Course completion certificate
Everything you’re
probably wondering
EDIT the answers to match DSU’s actual policies before publishing.
No. Module 1 starts with Python from first principles, and Module 2 builds the statistics you need from the ground up — no prior coding or advanced maths assumed. A large share of our learners come from non-IT backgrounds and have never written code before.
The program runs 6–7 months — the extra time over a plain data-science course covers the deep-learning, Generative AI and Agentic AI modules. Weekday batches are two hours a day; weekend batches are three hours per day across Saturday and Sunday. Plan for a similar amount of practice time outside class — that’s where the learning consolidates.
EDIT: state your fee here, along with any instalment or EMI options. Being upfront about pricing on this page will improve enquiry quality — people who can’t find a price often assume the worst and leave.
We provide placement assistance — resume preparation, mock interviews and introductions to our hiring-partner network — and that support continues after your final class. We don’t promise a guaranteed job, because no honest training provider can. What we can promise is that you’ll finish interview-ready with real projects to show. EDIT this to match your policy exactly.
Yes. Book a free demo session and a one-to-one counselling call. You’ll meet the trainer, see how a session runs and get the full syllabus before committing to anything.
Every class is recorded and available to you, so you can catch up on your own schedule. You can also raise anything unclear in the dedicated doubt-clearing sessions.
No. The course builds up to them — you learn Python, statistics and machine learning first, then move into deep learning, LLMs and agents. The Generative AI and Agentic AI modules start from the basics: how LLMs work, prompt engineering, RAG, and building tool-using agents with LangChain and LangGraph. The focus is on building real AI applications — RAG assistants, agents, LLM apps — not on AI research. You finish with both the classic data-science skills employers hire for and the Gen-AI edge that sets you apart.
Become a Gen-AI-ready data scientist
Book a free counselling session. We’ll walk you through the syllabus, batch options and fees — and tell you honestly whether this course is right for you.