Artificial intelligence is evolving beyond automation to solve business problems and perform complex tasks independently.
Applied Data Science & Agentic AI and Generative AI & Agentic AI are two specialised AI learning paths, but they are designed for different goals. While one focuses on data-driven analytics and decision-making, the other emphasizes large language models (LLMs), AI agents, and production-ready AI applications.
This blog covers:
Key differences between both programmes
Curriculum, projects, fees, and eligibility
How each programme uses Agentic AI
Applied Data Science & Agentic AI vs Generative AI & Agentic AI: A Quick Comparison
Choosing between these two IIT Kharagpur programmes depends on your career goals and the skills you want to develop. While both cover Agentic AI and hands-on learning, they differ in their focus, curriculum, and project outcomes. The table below provides a quick comparison of the key aspects to help you understand which programme best aligns with your interests.
Aspect | Applied Data Science & Agentic AI | Generative AI & Agentic AI |
Primary Focus | AI-powered analytics and data-driven decision-making. | Building and deploying LLM-powered AI applications. |
Core Foundation | Statistics, machine learning, data engineering, and business analytics. | LLMs, prompt engineering, RAG, fine-tuning, and Agentic AI. |
Programming | Python, SQL, and data processing. | Python, APIs, and AI frameworks. |
Machine Learning | Strong emphasis. | Foundation-level understanding with LLM focus. |
Deep Learning | Supports machine learning concepts. | Strong focus on transformers and foundation models. |
Generative AI | Part of the curriculum. | Core focus of the programme. |
Agentic AI | Agentic analytics and decision workflows. | Autonomous AI agents and multi-agent systems. |
Data Visualisation & Analytics | Strong emphasis. | Limited emphasis. |
Hands-on Projects | Predictive analytics, AI copilots, RAG BI systems, and agentic workflows. | Enterprise RAG, fine-tuned LLMs, AI agents, and GenAI APIs. |
Curriculum Comparison
Both programmes offer a structured learning path but focus on different areas of AI. The Applied Data Science & Agentic AI programme develops expertise in data analytics, machine learning, and AI-powered decision-making, while the Generative AI & Agentic AI programme focuses on building, fine-tuning, and deploying production-ready AI applications using large language models (LLMs).
Executive Post Graduate Certificate in Applied Data Science & Agentic AI | Executive Post Graduate Certificate in Generative AI & Agentic AI |
Module 1: Machine Learning & Statistical Foundations | Module 1: Foundations of GenAI & LLMs |
Module 2: Data Engineering, SQL & Modern AI Data Stack | Module 2: Advanced Prompting & RAG Systems |
Module 3: Data Visualization, Business Analytics & AI Storytelling | Module 3: LLM Fine-tuning & Alignment |
Module 4: Experimentation, Causal AI & Predictive Modelling | Module 4: Multimodal & Agentic AI |
Module 5: Generative AI, AI Copilots & Analytics Automation | Module 5: Deployment, Optimisation & AI Safety |
Module 6: Agentic AI, RAG Systems & Responsible AI | Key Topics: AI & Deep Learning Essentials, Transformer Architecture, Working with Foundation Models |
Capstone: Industry-focused AI-powered analytics project | Capstone: Industry-focused Generative AI & Agentic AI project |
Fee and Eligibility
Both programmes offer flexible payment options and are designed for graduates looking to build advanced AI skills. However, they differ in programme fees and eligibility requirements. The comparison below highlights the key admission criteria and fee details to help you choose the programme that best matches your academic background and technical experience.
Comparison Table
Feature | Applied Data Science & Agentic AI | Generative AI & Agentic AI |
Total Fee | ₹1,77,000 | ₹1,99,000 |
Seat Booking | ₹10,000 | ₹10,000 |
EMI | ₹6,031/month | ₹6,825/month |
Eligibility | Graduation with a minimum of 50% marks. | Graduation with a minimum of 50% marks. Applicants should also be comfortable with Python, APIs, technical documentation, and basic ML concepts. Candidates from non-technical backgrounds require at least 2 years of technology experience. |
How Agent Components Differ in Both Approaches?
While both programmes cover Agentic AI, they approach agent design differently. The Applied Data Science & Agentic AI programme focuses on data-driven reasoning and business decision-making, whereas the Generative AI & Agentic AI programme emphasizes language-based reasoning, context awareness, and autonomous task execution.
Planning and Reasoning
Applied Data Science & Agentic AI | Generative AI & Agentic AI |
Uses mathematical optimization and predictive models to make decisions. | Uses LLMs to understand context and reason through tasks. |
Applies reinforcement learning and statistical techniques for optimal outcomes. | Uses prompting techniques like Chain-of-Thought (CoT) and ReAct reasoning for step-by-step problem-solving. |
Focuses on data-driven decision intelligence. | Focuses on natural language reasoning and autonomous planning. |
Memory Systems
Applied Data Science & Agentic AI | Generative AI & Agentic AI |
Uses feature stores and structured datasets to train and improve models. | Uses vector databases to retrieve relevant information. |
Relies on time-series databases and historical business data for predictions. | Uses Retrieval-Augmented Generation (RAG) memory to provide context-aware responses. |
Optimized for analytical and predictive workflows. | Maintains conversational memory for multi-turn interactions and AI assistants. |
Tool Integration
Applied Data Science & Agentic AI | Generative AI & Agentic AI |
Connects with enterprise software, databases, ERP, and CRM systems for analytics and automation. | Integrates with APIs, external tools, and enterprise applications using function calling. |
Automates business processes and data-driven workflows. | Enables AI agents to use external tools, retrieve information, and complete complex tasks autonomously. |
Commonly supports business intelligence and decision-making systems. | Commonly powers AI assistants, copilots, and multi-agent workflows. |
Why IIT Kharagpur's AI Programmes Stand Out?
Whether you choose the Executive Post Graduate Certificate in Applied Data Science & Agentic AI or the Executive Post Graduate Certificate in Generative AI & Agentic AI, both programmes are designed to equip professionals with industry-relevant AI skills through a practical, production-focused learning experience. Backed by IIT Kharagpur's academic excellence, these programmes combine strong theoretical foundations with hands-on application to prepare learners for real-world AI roles.
Key highlights include:
100% live, faculty-led sessions by IIT Kharagpur professors.
Industry-focused curriculum that blends theory with practical AI applications.
Hands-on projects and a capstone to build a portfolio of real-world AI solutions.
Executive Certificate from IIT Kharagpur with an on-campus graduation ceremony.
Executive Alumni Status, providing access to the IIT Kharagpur Executive Education alumni network.
Flexible learning format with weekend classes designed for working professionals.
Production-oriented learning, enabling learners to build AI systems ready for enterprise use.
Conclusion
Choosing between the Applied Data Science & Agentic AI and Generative AI & Agentic AI programmes depends on your career goals and technical interests. If you want to build expertise in data analytics, predictive modelling, and AI-driven decision-making, the Applied Data Science programme is a great fit. If your goal is to develop LLM-powered applications, AI agents, and production-ready AI systems, the Generative AI programme is the better choice. Both IIT Kharagpur programmes offer live faculty-led learning, hands-on projects, and industry-relevant skills to help you succeed in the evolving AI landscape.