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What you'll learn

The Master Data Science with Generative AI & Agentic AI Course by Technogeeks X AI is designed to help students and professionals master Data Science, Machine Learning, Deep Learning, and the latest advancements in Generative AI and Agentic AI with real-world, industry-level projects. This 7-month program prepares learners to become job-ready AI and Data Science professionals.

This training starts with Python programming fundamentals and gradually moves towards advanced topics including LLMs, Retrieval-Augmented Generation (RAG), LangChain, and autonomous AI agent development.

Students will gain hands-on experience through 38 modules, milestone projects, real-world datasets, and industry-level use cases to build strong practical knowledge across 14 in-demand skills.

Data Science combined with Generative AI and Agentic AI is one of the fastest-growing career fields globally, with over 1.5M+ job openings in 2024 and projected growth of 3M+ new roles by 2028 — offering excellent salary packages and opportunities across industries.

Real-World Industry-Level Projects
Hands-on GenAI & Agentic AI Implementation
Instructor-Led Live Sessions
End-to-End Data Science & AI Pipeline Training
25+ Data Science Tools & Technologies

Resume & Portfolio Enhancement Support
Mock Interview Preparation
1:1 Mentorship from AI & Data Science Experts
100% Placement Assistance
Real-World Case Studies & Assignments

Master Data Science with Generative AI & Agentic AI

The Master Data Science with Generative AI & Agentic AI Course by Technogeeks X AI helps learners gain deep expertise in data science, machine learning, and cutting-edge AI technologies — enabling them to design, build, and deploy intelligent AI-driven solutions for real-world business challenges.

Data Scientists with Gen AI and Agentic AI skills collect and analyze data, build predictive models, develop Gen AI features like automation and content generation, and design Agentic AI systems that can plan tasks and take actions independently — helping businesses make faster, smarter, data-driven decisions.

During the training, students learn how to work across the complete AI and Data Science pipeline — from data collection and analysis to building LLM-powered applications and deploying autonomous AI agents.

The course includes milestone projects, a major capstone project, GitHub integration, mock interviews, and portfolio building to prepare fully industry-ready AI and Data Science professionals.

Students will learn:

Python Programming (Beginner to Advanced)
Data Analysis with NumPy, Pandas & Advanced Excel
Data Visualization with Python (Matplotlib, Seaborn) & Power BI
Mathematics & Statistics for Data Science
Machine Learning Algorithms (Supervised & Unsupervised)
Natural Language Processing (NLP) with NLTK
Deep Learning with TensorFlow & Keras
SQL, REST API & Flask Web Integration
Cloud Integration (AWS) & Big Data (Hadoop, Hive)
ETL Development with Python Scripting
AI Foundations & Generative AI Fundamentals
Prompt Engineering & Advanced GenAI Concepts
LLM Architecture, Tokenization & Embeddings
Retrieval-Augmented Generation (RAG)
Agentic AI — Agent Loop, ReAct Framework & Multi-Agent Systems
Frameworks: LangChain, LlamaIndex, CrewAI & LangGraph
Hands-on Projects: Resume Builder, Chatbot, Travel Assistant & Financial Data Analysis
Ethics, Safety, Bias & Guardrails in AI
GitHub, Version Control & Project Deployment

Flexible Learning Options

Technogeeks X AI offers flexible training modes to suit different learners:

Classroom Training (Pune)
Live Online Instructor-Led Sessions
Weekend and Weekday Batches

Students also get access to recorded sessions, project materials, a Telegram placement channel for job updates, and continuous expert support for learning at their own pace.

Course Content

What is Python and brief history
Why Python and who uses Python
Discussion on Python 2 and 3
Unique features of Python
Discussion on various IDEs
Demonstration of practical use cases
Python use cases using data analysis

Installing Python & setting up Python environment
Installation of Jupyter Notebook
Setting up development environment
How to access course material using Jupyter
Write your first program in Python

Python built-in functions
Number objects and operations
Variable assignment and keywords
String objects and operations / Print formatting with strings
List objects and operations
Tuple objects and operations
Dictionary objects and operations
Sets and Boolean
Object and data structures assessment test

Introduction to Python statements
If, elif and else statements
Comparison operators
Chained comparison operators
What are loops
While loops
Useful operators
List comprehensions
Statement assessment test
Game challenge

Methods – types of functions
Creating and calling user defined functions
Function practice exercises
Lambda Expressions
Map and filter
Nested statements and scope
Args and kwargs
Functions and methods assignment
Milestone Project – Making tic-tac-toe in Python

Process files using Python
Read/write and append file object
File functions
File pointer and operations
Introduction to error handling
Try, except and finally
Python standard exceptions
User defined exceptions
Unit testing
File and exceptions assignment

Python inbuilt modules
Creating UDM – User defined modules
Passing command line arguments
Writing packages
Define PYTHONPATH
__name__ and __main__

Object oriented features
Implement object oriented with Python
Creating classes and objects
Creating class attributes
Creating methods in a class
Inheritance
Polymorphism
Special methods for class
Assignment – Creating a Python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs

Collections module & Datetime
Python debugger
Timing your code
Regular Expressions
StringIO
Python decorators
Python generators

Python inbuilt modules
Install packages on Python
Introduction to pip, easy_install
Multithreading
Multiprocessing

Introduction in Excel
Data Cleaning & Preparation
Formatting & Conditional Formatting
Lookup Function
Analyzing data with Pivot Tables
Charts
Data Visualization/Dashboarding using Excel
Data Analysis using statistics

Introduction to data analysis
Data analysis and Artificial Intelligence Bridge and connecting it to database
Introduction to Data Analysis libraries
Data analysis introduction assignment challenge
Why Data analysis?

Introduction to Numpy arrays
Creating and applying functions
Numpy Indexing and selection
Numpy Operations
Exercise and assignment challenge

Introduction to Series
Introduction to DataFrames
Data manipulation with pandas
Missing data
Groupby
Operations
Data Input and Output
Pandas in depth coding exercises
Text data mining and processing
Data mining applications in Data engineering
File system integration with Pandas
Excel integration with Pandas
Operations on Excel using dataframe
Data aggregation on Excel Data
Data visualization using Excel data
Milestone Project – 2

Matplotlib
Plotting using Matplotlib & Numpy arrays
Plotting using object-oriented approach
Subplots using Matplotlib
Exercise and assignment challenge
Matplotlib attributes and functions
Matplotlib exercises

Comparison Between Power BI & Programming Based Data Visualization
Need Of Power BI
Types Of Data Sources Supported By Power BI For Report Development
How To Build Report & Dashboard in Power BI
How To Build Charts In Power BI
Data Visualization Using Power BI Features
Types of Graphs
Multiple graphs combinations
Multiple file formats supported in Power BI
Data analysis without visualization
Data analysis with visualization

Need of Mathematics for Data Science
Exploratory data analysis (EDA)
Numeric Variables
Qualitative and Quantitative Analysis
Types of Data Formats
Measuring the Central Tendency – The Model
Measuring Spread – Variance and Standard Deviation
Euclidean Distance
Understanding Parametric Tests
Confidence Coefficient

Understanding Machine Learning
Scope of ML
Supervised and Unsupervised learning

Introduction to Artificial Intelligence
Introduction to Machine Learning
Need of Machine learning in forecasting
Demand of forecasting analytics in current industrial trends
Introduction to Machine Learning Algorithms Categories
Linear Regression with Python
Introduction to Regression
Exercise on Linear Regression using sci-kit learn Library
Project on Linear regression using USA_HOUSING data
Evaluation of Linear regression using python visualizations
Practice project for Linear regression using advertisement data set
K-Nearest Neighbours using Python
Project on Logistic regression using Dogs and horses' dataset
Getting the correct number of clusters
Standard scaling problem
Practice project on KNN algorithm
Decision Tree and Random Forest with Python
Intuition behind Decision trees
Implementation of decision tree using a real time dataset
Ensemble learning
Decision tree and random forest for regression
Decision tree and random forest for classification
Evaluation of the decision tree and random forest using different methods
Practice project on decision tree and random forest using social network data
Support Vector Machines
Linearly separable data / Non-linearly separable data
SVM project with telecom dataset to predict the users portability
Principal Component Analysis (PCA)
Introduction to PCA / Need for PCA
Implementation to select a model on breast-cancer dataset
Model evaluation – Bias variance trade-off, Accuracy paradox, CAP curve
Clustering in Unsupervised Learning
K-means clustering – mall customers data
Hierarchical clustering intuition & implementation
Association Algorithms
A priori theory and explanation
Market basket analysis
Implementation of Apriori
Evaluation of association learning
POC – Predict relationship between frequently bought products on supermarket dataset

Introduction to Natural Language Processing
NLTK Python library
Data stemming technique
Data Vectorization
Exercise on NLTK
POC – Apply NLP techniques to understand customer reviews and predict if a review is good/bad without human intervention

Neural Network and Deep Learning
What is TensorFlow?
TensorFlow Installation
TensorFlow basics
TensorFlow with Contrib Learn
TensorFlow Exercise
Keras Basics
Pipeline implementation using Keras
MNIST implementation with Keras

What is SQL?
Why we need SQL Integration with Python
Data types in SQL
DDL, DML, TCL sublanguages in SQL
Significance and type of Joins in SQL
Where clause in SQL
Group by clause in SQL
Create, Insert, Select, Update, Delete, Drop, Truncate commands in SQL
Select command variants in SQL
SQL integration with Python
Table operations in SQL using Python
CRUD operations in SQL
Working on multiple tables using Python and SQL

REST principles
Creating application endpoints
Implementing endpoints
Using Postman for API testing
Python, Database and Frontend integration concept & implementation
Commit and rollback concept in SQL

CRUD operations on database
REST principles and connectivity to databases
Creating a web development API for login registers and connecting it to the database
Deploying the API on a local server

Cloud integration with AWS cloud computing

Hadoop
HDFS (Hadoop Distributed File System)
Hive

ETL Development with Python Scripting in Pandas

Project use cases Introduction
Project Scenarios / Project life cycle
What is version controlling in project management
What is GitHub
Significance of GitHub in project management
Code submission for testing and deployment
Predictive analytics tools and techniques
Project best practices

Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Introduction to Neural Networks (ANN) & Deep Learning (DL)
Introduction to Natural Language Processing (NLP)
Traditional AI vs Generative AI
Real-world applications of AI and GenAI
Introduction to GenAI and Agentic AI

What is Generative AI and how it works
Large Language Models (LLMs) overview
Use cases: text, code

Prompting and Prompt Engineering
Types of prompts
How to define effective prompts as user

Multimodality: text, images
RAG (Retrieval-Augmented Generation)
Mathematical fundamentals for RAG (Cosine Similarity, Softmax, Euclidean Distance, Dot Product, Probability, Similarity Vector Operations)
LLM Training Process
The Transformer Architecture
Question answering from documentation using GenAI model with LLM

Tokenization and Embeddings
Vector Spaces
Model Architectures
Model Workflow: Pre-training, Fine-tuning, and Inference

RAG Concept
RAG Components: Data Chunking and Indexing
Vector and Retrieval Methods
Model Adaptation and Evaluation: Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT)

What is an AI Agent?
Difference between GenAI and Agentic AI
Agent vs. Chatbot
The Agent Loop (Conceptual): Observe > Plan > Act > Reflect Cycle
The ReAct Framework (Conceptual): Reasoning and Acting
Real-life examples of AI agents

Reasoning and Planning
Tool use and API calling
Agent Reasoning & Memory
Frameworks: LangChain, LlamaIndex, CrewAI, LangGraph
Multi-Agent Systems
Workflow / Orchestration Frameworks
Agent Communication
Agent Persistence, Monitoring, and Deployment

Project 1: Resume builder using GenAI
Project 2: Chatbot with memory (LangChain)
Project 3: Travel assistant (fetches hotels/weather using API)
Project 4: Financial data analysis using API

Bias and fairness in AI
Hallucinations
Guardrails (safety in LLMs and agents)
Careers and Future of GenAI + Agentic AI

Requirements

Basic computer knowledge.
No prior Data Science experience required.
Basic mathematics & statistics understanding.
Laptop with stable internet access.

Description

Complete Data Science with Generative AI & Agentic AI training.
Covers Python, ML, Deep Learning, NLP, SQL, Power BI & more.
Real-world projects including RAG, LangChain & Agentic AI apps.
Career-ready Data Scientist & AI Engineer skills.

Instructor

Prince Sir

Advanced Educator

2,100+ Reviews 4.7 Rating
20,000+ Students

Our instructor is an experienced Data Science & AI professional specializing in Machine Learning, Deep Learning, Generative AI, Agentic AI, and real-world industry projects.

Review

4.7

Course Rating

63%

29%

Featured review

Amit Verma

Great learning experience. Concepts were explained in a very simple and practical way.

Neha Patil

Technogeeks X AI provides excellent training with hands-on practice and real examples.

Rahul Joshi

The course helped me understand real industry scenarios and boosted my confidence.

Snehal Kulkarni

Very well-structured course. Easy to follow even for beginners.

Karan Mehta

Technogeeks X AI training is practical and job-oriented. Highly recommended.

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360-degree program

AI IntegratedYes
Practical LearningYes
LecturesLive
Skill LevelBasic to Expert
QuizzesYes
CertificateYes
Live Doubt Clearing Yes
Placement CallsYes

Frequently Asked Questions

What is Master Data Science with Gen AI & Agentic AI Training in Pune?

Ans:
It is an advanced program that combines Data Science, Generative AI, and Agentic AI skills. This course helps you learn data analysis, machine learning, AI model building, and intelligent automation using real-world projects, making you job-ready for 2026 AI roles.

Why should I learn Data Science with Generative AI in 2026?

Ans:
Data Science with Gen AI is one of the most in-demand skills in 2026. Companies are rapidly adopting AI tools for automation, analytics, and decision-making, creating huge demand for professionals skilled in AI, ML, and data-driven technologies.

Do I need coding experience to join this Data Science course?

Ans:
No prior coding experience is required. The course starts from basics like Python and gradually moves to advanced topics like Machine Learning, Generative AI, and Agentic AI systems.

What topics are covered in this Data Science with AI course?

Ans:
The course covers complete Data Science and AI concepts. Topics include Python, Data Analysis, Machine Learning, Deep Learning, NLP, Generative AI, Agentic AI, and real-time projects with industry tools.

What job roles can I get after completing this Data Science training?

Ans:
You can apply for multiple high-demand roles. Job roles include Data Scientist, AI Engineer, Machine Learning Engineer, Data Analyst, Gen AI Developer, and AI Automation Engineer.

What is the salary after Data Science with Gen AI course in India?

Ans:
Data Science professionals earn high salaries in India. Freshers can start from ₹5–10 LPA, while experienced professionals can earn ₹15–30 LPA or more depending on skills and projects.

How long does it take to complete this Data Science training in Pune?

Ans:
The course duration is designed to make you job-ready quickly. It typically takes 4 to 6 months including hands-on projects, assignments, and interview preparation.

What is Generative AI and Agentic AI?

Ans:
Generative AI creates content, while Agentic AI performs tasks autonomously. Generative AI includes tools like ChatGPT and image generation models, while Agentic AI focuses on building AI agents that can make decisions and automate workflows.

Is this Data Science course suitable for beginners?

Ans:
Yes, this course is beginner-friendly. It starts from fundamentals and gradually moves to advanced AI concepts with practical implementation.

Is Data Science with AI a good career option in 2026?

Ans:
Yes, it is one of the best career options in 2026. With increasing adoption of AI and data-driven decisions, demand for skilled Data Science and AI professionals is growing rapidly across industries.

The Complete Master Data Science with Gen AI & Agentic AI Training in Pune 2026: From Zero to Expert!

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Master Data Science with Gen AI & Agentic AI Training in Pune

What you'll learn

Master Data Science with Generative AI & Agentic AI

Students will learn:

Flexible Learning Options

Course Content

Module 1: Getting Started Python Programming

Module 2: Setting Up And Installations

Module 3: Python Object And Data Structures Operations

Module 4: Python Statements

Module 5: UDF Functions And Methods

Module 6: File And Exception Handling

Module 7: Python Modules And Packages

Module 8: Object-Oriented Programming

Module 9: Advanced Python Modules

Module 10: Package Installation And Parallel Processing

Module 11: Data Analysis Using Advance Excel

Module 12: Data Analysis With Python

Module 13: Data Analysis Using Numpy

Module 14: Pandas And Advanced Analysis and Excel Integration

Module 15: Data Visualization With Python

Module 16: Data Visualization Using Power BI

Module 17: Mathematics And Statistics For Data Science

Module 18: Introduction To Machine Learning With Python

Module 19: Machine Learning Algorithms

Module 20: Natural Language Processing with NLTK

Module 21: Deep Learning with TensorFlow and Keras

Module 22: SQL (Structured Query Language)

Module 23: REST API With SQL CRUD Operations and Flask

Module 24: REST API Integration With Databases For Web App Development

Module 25: Cloud Integration

Module 26: Big Data Integration

Module 27: ETL Development with Python Scripting

Module 28: Major Project and Github

Module 29: AI Foundations

Module 30: Generative AI Fundamentals

Module 31: Prompting and GenAI Basics

Module 32: Advanced GenAI Concepts

Module 33: Deep Dive into LLM Architecture and Workflow

Module 34: Retrieval-Augmented Generation (RAG)

Module 35: Introduction to Agentic AI

Module 36: Agentic AI Core Concepts

Module 37: Hands-on Projects

Module 38: Ethics, Safety and Future