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PG Program in Data Science with Generative AI

Build machine learning, deep learning, NLP and generative AI skills on deployment-focused projects, and go beyond models into the agentic AI workflows teams are actually shipping.

Duration12 Months
ModeLive Online
Learners10,000+
Enroll Now - ₹1,55,000Download Brochure
Next BatchJuly 18, 2026
Duration12 Months · Live Online
Industry Projects30+
EligibilityFreshers, Graduates, Experienced

What you’ll gain from this program

This PG Program in Data Science, AI/ML with Agentic AI equips you with machine learning, deep learning, NLP, generative AI and agent-building skills through deployment-focused projects and expert guidance.

Python & Statistical Foundations
Machine Learning Model Development
Deep Learning & Neural Networks
NLP & Large Language Models
Agentic AI & RAG Systems
300+ Hrs AI/ML-Focused Content
400+ Hrs Live Project Sessions
Practising AI Engineers as Mentors
Data Science & AI Job Placement Support
Round-the-Clock Assistance
Data Science & AI Curriculum Track
Skill Evaluation Tests
Learning Performance Analytics Dashboard
Professional Community Access
Self-Paced Learning Options
Global Alumni Community
Mock Interview Training
Data Science & AI Certificates
Advanced LMS Platform
Live AI Deployment Case Studies
PythonMLNLPLLMsAgentic AI

PG Program in Data Science with Generative AI curriculum

A comprehensive curriculum designed by industry experts combining Python, statistics, machine learning, deep learning, NLP, generative AI and agentic systems. Master model building, evaluation, deployment and MLOps with hands-on projects.

📚 21 modules · learning roadmap

01Introduction to Data Science & AI
  • What data scientists and AI engineers do
  • The data science lifecycle end to end
  • Where ML helps and where it does not
  • The modern AI/ML toolchain
  • Career paths: analyst, scientist, ML engineer
  • Industry trends and outlook
02Python for Data Science
  • Python syntax, data structures and control flow
  • NumPy and vectorised computation
  • pandas for data manipulation
  • Working in Jupyter notebooks
  • Reading data from files, APIs and databases
  • Python foundations project
03Statistics & Probability
  • Descriptive statistics and distributions
  • Probability rules and Bayes' theorem
  • Sampling and the central limit theorem
  • Confidence intervals and hypothesis testing
  • A/B testing and experiment design
  • Statistical inference case study
04Data Wrangling & Feature Engineering
  • Cleaning, imputation and outlier handling
  • Encoding categorical variables
  • Scaling, normalisation and transformations
  • Feature creation from raw signals
  • Feature selection and dimensionality reduction
  • Feature engineering project
05Exploratory Data Analysis & Visualisation
  • Structured exploration of a new dataset
  • matplotlib and seaborn for analysis
  • Correlation, distribution and relationship plots
  • Detecting leakage and data problems early
  • Communicating exploratory findings
  • Complete EDA case study
06Supervised Learning: Regression
  • Linear and polynomial regression
  • Regularisation: Ridge, Lasso and ElasticNet
  • Assumptions, residuals and diagnostics
  • Regression evaluation metrics
  • Interpreting coefficients responsibly
  • Regression modeling project
07Supervised Learning: Classification
  • Logistic regression and decision boundaries
  • Decision trees and k-nearest neighbours
  • Precision, recall, F1 and ROC-AUC
  • Class imbalance strategies
  • Threshold selection for business cost
  • Classification modeling project
08Ensemble Methods & Model Tuning
  • Bagging, boosting and stacking
  • Random Forest in depth
  • XGBoost, LightGBM and CatBoost
  • Cross-validation strategies
  • Hyperparameter search methods
  • Ensemble model competition project
09Unsupervised Learning
  • k-means and hierarchical clustering
  • DBSCAN and density-based methods
  • PCA and dimensionality reduction
  • Anomaly and outlier detection
  • Evaluating clusters without labels
  • Customer segmentation project
10Time Series Analysis & Forecasting
  • Trend, seasonality and stationarity
  • ARIMA and SARIMA models
  • Prophet and modern forecasting tools
  • Backtesting a forecast properly
  • Forecast error metrics
  • Demand forecasting project
11Deep Learning Foundations
  • Neurons, layers and activation functions
  • Forward pass and backpropagation
  • Loss functions and optimisers
  • Training, validation and regularisation
  • PyTorch and TensorFlow essentials
  • First neural network project
12Computer Vision
  • Convolutional neural networks explained
  • Image classification architectures
  • Transfer learning with pretrained models
  • Object detection fundamentals
  • Data augmentation strategies
  • Computer vision project
13Natural Language Processing
  • Text preprocessing and tokenisation
  • Embeddings and vector representations
  • Sequence models and attention
  • The transformer architecture
  • Text classification and named entity recognition
  • NLP project
14Large Language Models
  • How LLMs are trained and how they behave
  • Prompt engineering and structured output
  • Context windows, tokens and cost
  • Fine-tuning versus prompting versus RAG
  • Evaluating LLM output quality
  • LLM application project
15Generative AI Applications
  • Text, image and multimodal generation
  • Embeddings and vector databases
  • Retrieval-augmented generation end to end
  • Chunking, indexing and retrieval quality
  • Guardrails, safety and hallucination control
  • RAG application project
16Agentic AI Systems
  • What makes a system agentic
  • Tool use and function calling
  • Planning, memory and multi-step reasoning
  • Multi-agent orchestration patterns
  • Evaluating and debugging agent behaviour
  • Agentic AI build project
17SQL & Big Data for Data Science
  • SQL for analytical workloads
  • Window functions and analytical queries
  • Working with large datasets
  • Distributed processing concepts with Spark
  • Data access patterns for ML pipelines
  • Big data analysis exercise
18Model Deployment & MLOps
  • Packaging a model as an API
  • FastAPI and containerisation with Docker
  • Experiment tracking and model registries
  • CI/CD for machine learning
  • Monitoring drift and model decay
  • Model deployment project
19Responsible AI & Model Governance
  • Bias and fairness in models and data
  • Explainability with SHAP and LIME
  • Privacy, consent and data protection
  • Model risk, misuse and failure modes
  • Documentation and model cards
  • Responsible AI review exercise
20Data Science & AI Capstone Project
  • End-to-end project on a real-world problem
  • Problem framing and dataset selection
  • Modeling, evaluation and iteration
  • Deployment of the finished model or agent
  • Technical write-up and presentation
  • Portfolio packaging of the capstone
21AI Career Readiness & Interview Prep
  • Data science resume creation and optimization
  • ML theory and coding interview preparation
  • Take-home modeling and case tests
  • Behavioral interview training
  • LinkedIn and GitHub portfolio for AI roles
  • 1 year Placement Support for Top Fellows

Internship program

  • Real-world Machine Learning Projects
  • Model Development & Evaluation Cycles
  • LLM and RAG Application Builds
  • Agentic AI Workflow Implementation
  • Model Deployment & Monitoring
  • Industry Mentorship by Practising AI Engineers

Soft skills program

  • Technical Presentation & Demo Skills
  • Explaining Models to Non-Technical Stakeholders
  • Problem Framing & Scientific Thinking
  • Cross-Functional Collaboration
  • Research Reading & Continuous Learning
  • Professional Networking & Relationship Building

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