Programme Content
Module 1: Data Fundamentals & Visualization using Power BI
Types of data and descriptive statistics
Power BI environment and its understanding
Basic data visualization using graphical techniques
Advanced data visualization and BI dashboarding
Module 2: Statistical Thinking & Data Transformation
Inferential statistics and hypothesis testing
Data exploration, cleaning, and transformation
Assumptions checking and data readiness for modelling
Statistics for business decision-making
Examining the readiness of the data by checking assumptions
Data transformation for advanced modelling
Module 3: Implementing Python for AI & LLM Workflows
Understanding python environment (Jupiter notebook and google collaborator).
Basic data handling and functions
Advanced functions in python
Data visualization using python
Module 4: SQL & Data Pipelines
SQL fundamentals for data extraction and manipulation
Data pipeline concepts and ETL workflows
Connecting and querying enterprise databases
Module 5: AI-Augmented Analytics
AI-powered BI reporting (Copilot, Gemini for BI)
Automated insight generation and natural language querying
Time series forecasting with AI-augmented methods (ARIMA, Prophet + AI enhancements)
Module 6: Machine Learning & LLM Fine-Tuning
Supervised learning: regression, classification, tree-based models
Unsupervised learning: Clustering, PCA, Association Rules
Transition from classical ML to LLM fine-tuning basics
Module 7: Generative AI for Business
LLM landscape and model selection for business use cases
Prompt engineering: Zero-shot, few-shot, chain-of-thought
Develop AI solutions processing text, images, audio, and video
Build intelligent assistants with API integrations and automation
Building Gen AI-powered analytics reports and dashboards
Module 8: Advanced Prompt Engineering and RAG
Learn advanced prompting techniques for maximum AI output quality
Customize LLMs for industry-specific use cases and domains
RAG (Retrieval-Augmented Generation) for enterprise knowledge systems
Implement RAG systems with enterprise data integration
Module 9: Agentic AI Design & Deployment
AI Agent architecture: Autonomous decision-making
Frameworks: LangChain, LangGraph, AutoGen, CrewAI
Multi-agent systems for business workflows (finance, marketing, ops)
Connect AI agents to databases, APIs, and enterprise systems
Develop autonomous AI agents for complex business automation
Module 10: AI-Augmented Decision Intelligence
AI + human decision-making frameworks
Causal AI vs. Predictive AI
AI for scenario planning and strategic forecasting
Responsible AI, hallucination risk, and governance
Module 11: Enterprise Gen AI Implementation
AI strategy and use-case identification for organisations
Change management for AI adoption
ROI measurement of AI initiatives
Vendor evaluation: Build vs. Buy vs. Fine-tune
Note: The list of modules provided is subject to change and may be updated or revised at the institute's discretion.
Package Pricing at Mission Hospital (tMB527-pDF-ENG)
Predicting Net Promoter score (Nps) to Improve Patient Experience at Manipal Hospital
Improving Lead Generation at Eureka Forbes Using Machine Learning Algorithms
Retention Modeling at Scholastic Travel Company
and many more...
Programme Details
| Duration | 1 Year | |
|---|---|---|
| Delivery | 140 hours (128 hours of live interactive online classroom | |
| +12 hours of in-campus classes) | ||
| Session Timings | Sundays, 6:45 PM to 9:45 PM | |
| Application Closure Date | Closing Soon | |
| Commencement Date | 11thOctober 2026 |
