Data Science with Generative AI and Agentic AI Course
Become a future-ready AI professional by mastering Python, SQL, Statistics, Machine Learning, Generative AI, Large Language Models (LLMs), RAG, LangChain, LangGraph, and Agentic AI. This industry-focused program is designed to help students and working professionals build intelligent AI applications through practical projects, live mentoring, and interview preparation.
Course Overview
Artificial Intelligence is rapidly transforming industries, and organizations are looking for professionals who understand both traditional Data Science and modern AI technologies. This course combines the core foundations of Data Science with cutting-edge concepts like Generative AI, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, and Agentic AI.
Starting from Python programming, you’ll learn Statistics, Exploratory Data Analysis (EDA), Machine Learning, Prompt Engineering, Vector Databases, Large Language Models (LLMs), and Multi-Agent AI Systems. By the end of the program, you’ll be able to build intelligent AI applications capable of reasoning, planning, retrieving knowledge, and performing real-world tasks.
The curriculum emphasizes hands-on learning through practical assignments, real-world datasets, capstone projects, and interview-focused preparation to help you transition into high-demand AI roles.
- Anyone looking to build a career in Artificial Intelligence
- Engineering Students
- College Students
- BCA, MCA, B.Tech Students
- Working Professionals
- Software Developers
- Python Developers
- Data Analysts
- BI Professionals
- AI Enthusiasts
- 3–4 Months Weekend Program
- Instructor-led Live Training
- Industry-Based Curriculum
- Hands-on Coding Sessions
- Machine Learning Projects
- Generative AI Applications
- Prompt Engineering
- LangChain & LangGraph
- Retrieval-Augmented Generation (RAG)
- Agentic AI Development
- Resume Building
- Mock Interviews
- Placement Support
Explore the modules module by module
Module 1 – Python for Data Science
Core Programming- Python Basics & Variables
- Data Types & Functions
- Loops & OOP Basics
- File Handling & Exception Handling
- NumPy & Pandas
- Data Cleaning & Data Visualization
Module 2 – Statistics & EDA
Data Analysis- Descriptive Statistics & Probability
- Hypothesis Testing (Z-Test, T-Test, Chi-Square Test)
- Exploratory Data Analysis (EDA) Techniques
- Detecting Missing Values & Identifying Outliers
- Understanding Distributions & Variable Relationships
- Avoiding Wrong ML Models Selection
- Project: EDA Project & Insights Presentation
Module 3 – Machine Learning
Predictive Modeling- Complete ML Lifecycle
- Regression & Classification Algorithms
- Decision Trees & Random Forest
- Clustering & Principal Component Analysis (PCA)
- Model Evaluation & Hyperparameter Tuning
- Project: End-to-End ML Project Deployment
Module 4 – Generative AI Fundamentals
GenAI Core- Introduction to Large Language Models (LLMs)
- Prompt Engineering Fundamentals
- Embeddings & Vector Databases
- LangChain Framework Overview
- Building AI Workflows
Module 5 – Deep Dive into GenAI and LLMs
Advanced LLMs- Generative AI vs Traditional AI
- Tokens, Context Window & Parameters
- Training of LLMs & Capabilities
- Common Generative AI Applications
- Prompt Engineering Principles & Components
- Types of Prompting Techniques
- Embeddings Architecture & Similarity Search
- Vector Database Operations
Module 6 – RAG Systems
Retrieval Architecture- RAG Architecture & High-Level Workflows
- Why & Where RAG Systems are Used
- Data Ingestion & Chunking Strategies
- Embedding Generation & Vector Retrieval
- Implementation: RAG System Development
Module 7 – Agentic AI & Multi-Agent Systems
Next-Gen AI- Agentic AI vs GenAI & Core Importance
- Core Components, Tool Usage & Memory
- Single-Agent vs Multi-Agent Systems
- Agentic AI Architectures & LangGraph
- Manager–Worker (Coordinator Pattern)
- Planner–Executor & Researcher–Writer Patterns
- Critic / Reviewer & Debate / Consensus Patterns
- Tool-Specialist Agents & Sequential Pipeline Pattern
Module 8 – Capstone & Interview Preparation
Career Placement- Resume Building & ATS Optimization
- AI Portfolio Development
- Technical & HR Mock Interviews
- Final Phase: Capstone Project Presentation
Modes Of Training
Classroom Training
Lives interactive sessions delivered in our classroom by our expert trainers with real-time scenarios.
Online Training
Learn from anywhere over internet, joining the live sessions delivered by our expert trainers.
Self-Pace Training
Learn through pre-recorded video sessions delivered by experts with your own pace and timings
Course Fee & Enrollment
Check Trainingya Placement
"As a Data Analyst at Infosys, handling enterprise data requires robust skills. This program's advanced modules on Python, SQL, and predictive analytics were career-changing!"
"Working as a Data Analyst at Annalect, marketing analytics and reporting tools are crucial. The hands-on training with modern data stacks gave me a major professional edge."
"As a Data Analyst at Niva Bupa, processing healthcare metrics accurately is vital. The portfolio projects and dedicated placement mock sessions helped me seal this role easily."
"As a Data Analyst at Deloitte, business intelligence and large-scale data querying are everyday tasks. The structured data visualization assignments built my problem-solving depth."
"Stepping into a Data Analyst role at Wipro from a non-tech background felt challenging, but the step-by-step guidance and technical training made everything achievable."
FAQs
Yes. The program starts with Python fundamentals and gradually progresses to Machine Learning, Generative AI, RAG, and Agentic AI.
No prior programming experience is required. Python is taught from the basics.
Yes. You’ll learn Large Language Models (LLMs), Prompt Engineering, LangChain, Vector Databases, RAG, and practical AI application development.
Agentic AI focuses on building intelligent AI agents that can reason, plan, use tools, collaborate with other agents, and complete complex tasks autonomously.
Yes. The course includes multiple hands-on projects along with a final capstone integrating Machine Learning, RAG, and Agentic AI.
Yes. TrainingYA provides resume building, mock interviews, career guidance, and placement assistance to eligible learners.
Upon successful completion, you’ll receive a Course Completion Certificate from TrainingYA.
