Data Science, Analytics and Complete GenAI with Python

From Data Analysis to AI Agents — learn everything step by step.

Complete Tech Stack You'll Master

From basics to advanced - everything you need to become an AI professional.

Generative AI AI Agents Agentic AI RAG Systems Vector Databases MCP Servers LangChain Hugging Face Machine Learning Deep Learning Data Analysis NumPy & Pandas

Program Duration

8 Months

Learning Format

Live, Online

Projects

20+ Projects

Learn Data & AI in One Go: Python, Machine Learning, Deep Learning, and Generative AI

Master the basics of data science and machine learning with hands-on projects like regression, classification, and clustering. Learn how to handle data and make models that actually work.

Discover deep learning using ANNs, CNNs, and RNNs to work with images, text, and sequences. Understand how neural networks learn and make predictions.

Build Generative AI apps – chatbots, RAG systems, and AI agents – using LangChain, MCP servers, and FastAPI. Work on real projects and create AI solutions you can use in the real world.

Upcoming Batches

Weekdays

Offline

Batch Start Date:Mon Mar 23 2026

Batch Time:

1. 5 PM to 6: 30 PM

Fee:

18,000

Apply Now

Weekdays

Online

Batch Start Date:Mon Mar 23 2026

Batch Time:

1. 7 PM to 8:30 PM

Fee:

11,000~~18000~~

Apply Now

What you'll learn

This curriculum designed by Industry expert for you to become a next industry expert. Here you will not only learn, you will implement your learnings in real time projects.

Python Essentials: From Basics to Logic Building

Python Fundamentals

Installation of Python and VSCode
Hello World Program
Data Types (int, float, str, bool)
Taking user input
Arithmetic Operations (+, -, *, /, //, %, **)
Type Conversion (int(), str(), float())

Strings

String indexing and slicing
String methods and operations
String formatting techniques

Lists

Creating and accessing lists
List methods (append, extend, insert, remove)
List slicing and indexing
List comprehension

Tuples, Sets & Dictionaries

Tuple (immutable sequences, packing, unpacking)
Set (unique elements, set operations)
Dictionary (key-value pairs, methods, nested dictionaries)

Conditional Statements

if, elif, else conditions
Nested conditions
Logical operators (and, or, not)

Loops

for loop (iterating over sequences, nested loops)
while loop (condition-based looping, break, continue)
Range function (range(), enumerate(), zip())

Functions

Defining functions
Parameters and return values
*args and **kwargs
Lambda (anonymous functions)

Advanced Function Concepts

Map function (transforming iterables)
Filter function (filtering data)
Decorator (function wrappers, @decorator syntax)
Generator (yield, memory-efficient iteration)

Exception Handling

try, except, else, finally blocks
Handling multiple exceptions
Creating custom exception classes

File Operations

Reading and writing text files
File modes (r, w, a, r+, w+)
Context managers (with statement)
Working with CSV and JSON files

Classes & Objects

Creating classes and objects
Constructor (__init__ method)
Instance variables vs Class variables

OOPs Principles

Inheritance (single, multiple, multilevel)
Encapsulation (public, private, protected)
Polymorphism (method overriding, duck typing)

Advanced OOPs

Methods (instance, class, static methods)
Magic Methods (__str__, __repr__, __len__)
Property decorators (@property)

Built-in Libraries

math (mathematical operations, constants)
random (generating random numbers, choices)
datetime (working with dates and times)
os (file system operations)
json (parsing and creating JSON data)
csv (reading and writing CSV files)

External Libraries

Pillow (PIL) - Image processing, filters, editing
segno - QR code generation
requests - Making HTTP requests, API calls

Creating Custom Libraries

Creating Python modules
Organizing code into packages
Understanding __init__.py file
Importing your custom library
Distributing your library (setup.py, PyPI basics)

Introduction to Streamlit

Setting up Streamlit
Creating basic Streamlit apps
Streamlit components and widgets

Real-World Projects

Project 1: Image Filter App (apply filters using Pillow)
Project 2: QR Code Generator (using segno)
Project 3: Weather Dashboard (using weather API)
Project 4: Unsplash Image Gallery (Integrate Unsplash API)
Project 5: Currency Converter (using exchange rate API)
Project 6: Quote Generator (random quotes API)

SQL Essentials: From Basics to Advanced Queries

SQL Basics

Introduction to Databases & SQL
Installing MySQL/SQLite
SELECT statement
WHERE clause (filtering)
ORDER BY and LIMIT
Data Types (INT, VARCHAR, DATE, etc.)

Working with Data

INSERT INTO (adding records)
UPDATE (modifying records)
DELETE (removing records)
Basic operators (=, >, <, AND, OR, NOT)

Aggregate Functions

COUNT, SUM, AVG, MIN, MAX
GROUP BY clause
HAVING clause

SQL Joins

INNER JOIN
LEFT JOIN
RIGHT JOIN
Multiple table joins

Subqueries

Subquery basics
Subquery in WHERE clause
IN and NOT IN operators

String & Date Functions

String functions (CONCAT, UPPER, LOWER, SUBSTRING)
Date functions (NOW, DATE, YEAR, MONTH)
LIKE and wildcards (%, _)

Database Design Basics

CREATE DATABASE and CREATE TABLE
Primary Key and Foreign Key
ALTER TABLE basics
DROP TABLE
Constraints (NOT NULL, UNIQUE, DEFAULT)

Advanced Queries

CASE statements
UNION and UNION ALL
NULL handling (IS NULL, COALESCE)
DISTINCT

Views, CTEs & Indexes

Creating and using Views
Common Table Expressions (CTEs) - WITH clause
Understanding Indexes
Creating Indexes for performance
When to use Indexes

Real-World SQL

Importing CSV data
Exporting query results
Writing clean queries
Common SQL patterns
Practice with sample databases

Statistics & Probability Essentials: Basics to Data Thinking

Introduction to Statistics

What is Statistics
Types of data (qualitative, quantitative)
Population vs Sample
Scales of measurement

Descriptive Statistics

Mean, Median, Mode
Range, Variance, Standard Deviation
Quartiles and Percentiles
Skewness and Kurtosis

Data Visualization for Statistics

Bar charts and Histograms
Box plots
Line charts
Scatter plots

Probability Basics

Probability concepts
Sample space and events
Types of probability
Addition and multiplication rules

Conditional Probability & Bayes Theorem

Conditional probability
Independent and dependent events
Bayes theorem

Probability Distributions

Bernoulli distribution
Binomial distribution
Poisson distribution
Normal distribution

Expectation & Variability

Expectation (mean of random variable)
Variance and Standard Deviation
Covariance
Correlation

Sampling & Central Limit Theorem

Sampling techniques
Sampling distributions
Central Limit Theorem

Hypothesis Testing

Null and Alternative hypothesis
Type I and Type II errors
Z-test
T-test
Chi-square test

Data Analysis Essentials: NumPy Foundations to Pandas Mastery

NumPy Basics

What is NumPy
Why NumPy is used
Creating arrays
Array shape and data types
Accessing elements
Basic slicing
Element-wise operations
Basic mathematical operations
sum, mean, min, max
reshape
flatten
Generating random numbers
NumPy vs Python lists
NumPy for data handling

Introduction to Pandas

What is Pandas
Series and DataFrame
Creating DataFrames

Data Loading & Inspection

Reading CSV and Excel files
head(), tail(), info()
describe()

Data Cleaning

Handling missing values
Dropping and filling data
Removing duplicates
Data type conversion

Data Manipulation

Selecting columns and rows
Filtering data
Adding and removing columns
Sorting data

Aggregation & Grouping

GroupBy operations
Aggregation functions
Multiple aggregations

Time Series Basics

DateTime conversion
Working with dates
Time-based filtering

Practical Data Analysis

Combining NumPy with Pandas
Working with real datasets
Data preparation for visualization and ML

Data Visualization Essentials: Charts to Storytelling

Visualization Foundations

Why data visualization matters
Types of charts and use cases
Visualization best practices
Libraries used: Matplotlib, Seaborn, Plotly

Basic Charts

Line charts
Bar charts
Horizontal & stacked bars
Pie & donut charts

Distribution & Comparison

Histogram
Box plot
Violin plot
Count plot

Relationship Analysis

Scatter plots
Bubble charts
Heatmaps
Pair plots

Storytelling & Dashboards

Choosing the right chart
Color, labels, and annotations
Interactive plots with Plotly
Presenting insights clearly

EDA Essentials: Turning Data into Insights

Smart Sales Insight Dashboard (Streamlit)

Clean and prepare raw sales data
Analyze daily, monthly, and yearly trends
Find best and worst selling products
Region-wise sales comparison
Interactive dashboard using Streamlit

Customer Buying Behavior Analyzer

Analyze customer purchase frequency
Identify high-value customers
Repeat vs one-time buyer analysis
Simple customer grouping
Clear charts for easy understanding

Business Performance Health Monitor

Revenue, profit, and loss analysis
Detect sudden spikes and drops
Month-wise and category-wise insights
Visual signals for business risks

Marketing Campaign Impact Analyzer

Compare multiple marketing campaigns
Analyze clicks, conversions, and sales
Identify best performing campaigns
Simple visuals for decision making

Product Demand & Inventory Insight Tool

Identify high and low demand products
Track demand changes over time
Detect over-stock and under-stock items
Data-driven inventory insights

Machine Learning Essentials: Models That Actually Work

Machine Learning Basics

What is Machine Learning ?
Types of Machine Learning ?
Independent and Dependent variables
What are features ?
What is target variable ?

Data Preprocessing

Handling missing values
Train-test split
Feature scaling basics
Encoding categorical data

Feature Scaling & Encoding

Standard Scaler
Min-Max Scaler
Label Encoding
One-Hot Encoding

Regression Algorithms

Linear Regression
Multiple Linear Regression
Polynomial Regression
Ridge Regression
Lasso Regression

Regression Metrics

Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
R² Score

Classification Algorithms

Logistic Regression
K-Nearest Neighbors (KNN)
Naive Bayes
Decision Tree Classifier
Random Forest Classifier
Support Vector Machine (SVM)

Classification Metrics

Confusion Matrix
Accuracy
Precision
Recall
F1 Score

Clustering Algorithms

K-Means Clustering
Hierarchical Clustering
DBSCAN

Projects

No-Code Machine Learning Playground (Streamlit)
House Price Prediction (Regression)
Customer Churn Prediction (Classification)
Loan Approval Prediction
Customer Segmentation (Clustering)
Sales Forecasting Model

Deep Learning & NLP Essentials

Deep Learning Basics

What is Deep Learning
Difference between Machine Learning and Deep Learning
What is a Neuron
What is a Neural Network
Types of Neural Networks (ANN, CNN, RNN)

Activation Functions

Why activation functions are needed
Sigmoid function
ReLU function
Tanh function
Softmax function

Artificial Neural Network (ANN)

How ANN works
How ANN learns
Loss function concept
Gradient Descent
Backpropagation
Project: ANN for Prediction

Recurrent Neural Network (RNN)

What is sequence data
Why RNN is needed
How RNN works
Basic idea of LSTM and GRU
Common RNN use cases

NLP Basics (GenAI Foundation)

What is Natural Language Processing
Text data basics
Tokenization
Stopwords
Stemming and Lemmatization
Text to numerical representation (basic idea)

Projects

Customer Churn Prediction using ANN
Cat vs Dog Image Classification using CNN
Brain Tumor Classification on Images using CNN
Handwritten Digit Classification using CNN
Facial Expression Detection using OpenCV + CNN
Sentiment Analysis using RNN

GenAI Essentials: Build AI Agents, RAG Systems & MCP Servers

Introduction to GenAI

What is GenAI?
How it is different from regular AI/ML
Overview of projects

Large Language Models (LLMs)

What are LLMs?
How LLMs are trained
Transformers basics
Popular LLMs (GPT, Claude, Gemini, LLaMA)
Capabilities & limitations

Setup & LangChain Basics

OpenAI, Gemini, Groq, Claude API setup
HuggingFace setup
Environment variables & security
Cost management
LangChain installation & structure
First LLM call
Prompt templates & chains

AI Agents

What are AI Agents & ReAct framework
Function calling & tool creation
Agent executor & logic
Streamlit interface for agents

AI Agent Projects

Calculator Agent
Google Search Agent
SQL Agent
GitHub Agent
Email Agent
Jira Agent

RAG Systems

What is RAG & why it matters
Vector embeddings & databases (Pinecone, ChromaDB, FAISS)
Document loaders (PDF, DOCX, CSV, web, API)
Text chunking & embedding storage
Build RAG applications: PDF Chat, Website Bot, Multi-Document RAG

LangGraph & Advanced Workflows

What is LangGraph & its components
Nodes, edges, state management
Conditional routing & multi-tool agents
Human-in-the-loop workflows
Projects: LangGraph Chatbot, LangGraph RAG, AI Blog Generator

MCP Servers

What is MCP & why it matters
MCP architecture
Build custom MCP server
Connect MCP with LangChain
Use MCP tools & manage resources
Project: Custom MCP Server

GenAI Deployment & Bonus Projects

APIs & FastAPI

FastAPI basics for AI applications
Routing, Pydantic models, request/response handling
Error handling & documentation
Build REST API for Chatbot with LangChain integration
Session management & async operations
Project: Chatbot REST API

AWS Bedrock & Deployment

Introduction to AWS Bedrock & account setup
Using Claude 3 models on Bedrock
Build scalable RAG and Chatbot systems
Production deployment considerations
Project: AWS Bedrock Chatbot

Serverless Deployment

What is AWS Lambda & serverless architecture
Deploy LangChain apps serverlessly
Handle auto-scaling & requests
Cost optimization & benefits
Project: Serverless AI API

Production Deployment with FastAPI

EC2 setup & load balancing
SSL/HTTPS & monitoring
CI/CD pipeline for AI apps
Best practices for production systems

Bonus Projects

Offline Chatbot with Ollama
Groq Chatbot (Ultra-fast)
Gemini Chatbot
HuggingFace Chatbot
Website QnA Bot
LangGraph RAG System
AI Blog Generator
Production RAG on AWS

Technologies You Will Master Hands-On

During this program you will learn some most demanding technologies. We will develop some real time projects with the help of these technologies.

AWS

Python

FastAPI

Generative AI

Langchain

Hugging Face

Google Gemini

LangGraph

ChromaDB

MCP Servers

AWS Bedrock

OpenAI

Program Fees

18,000

(incl. taxes)

If you will join in a group, complete group will get discount.

You can pay your fee in easy installment's. For more details you can connect with our team.

Meet Your Instructors

You will learn with industry expertes.

Nikita Choudhary

Sr. Software Engineer

Prateek Mishra

Sr. Software Engineer

What You Could Become

Transform Into a Full Stack Data Scientist - Your Path to Limitless Possibilities!

Data Scientist

Data Analyst

Statistical Analyst

Machine Learning Engineer

Deep Learning Engineer

Generative AI Engineer

LLM Engineer

AI Agents Developer

Agentic AI Developer

RAG Systems Engineer

And many more...

Frequently Asked Questions

Who is this program for?

This program is designed for students, working professionals, and beginners who want to build a career in Data Science, Generative AI, and AI Agents.

Do I need prior coding experience?

No prior coding experience is required. We start with Python from scratch and gradually move to advanced AI and Generative AI concepts.

Will I build real-world projects?

Yes, the program focuses on hands-on learning. You will build real-world AI, GenAI, and Agentic AI projects.

Does this program cover AI Agents and RAG systems?

Yes. You will learn how to build AI Agents, Agentic workflows, and Retrieval-Augmented Generation (RAG) systems using modern tools.

What career roles can I apply for after this course?

You can apply for roles like Generative AI Engineer, LLM Engineer, AI Agents Developer, and Applied AI Engineer.

Will I get placement assistance?

Yes, we provide career guidance, resume support, and interview preparation.

Is the course online or offline?

We offer both live online and classroom training options.

Will I receive a certificate?

Yes, you will receive a course completion certificate from TechSimPlus.

Data Science, Analytics and Complete GenAI with Python

Python Fundamentals

Strings

Lists

Tuples, Sets & Dictionaries

Conditional Statements

Loops

Functions

Advanced Function Concepts

Exception Handling

File Operations

Classes & Objects

OOPs Principles

Advanced OOPs

Built-in Libraries

External Libraries

Creating Custom Libraries

Introduction to Streamlit

Real-World Projects

SQL Basics

Working with Data

Aggregate Functions

SQL Joins

Subqueries

String & Date Functions

Database Design Basics

Advanced Queries

Views, CTEs & Indexes

Real-World SQL

Introduction to Statistics

Descriptive Statistics

Data Visualization for Statistics

Probability Basics

Conditional Probability & Bayes Theorem

Probability Distributions

Expectation & Variability

Sampling & Central Limit Theorem

Hypothesis Testing

NumPy Basics

Introduction to Pandas

Data Loading & Inspection

Data Cleaning

Data Manipulation

Aggregation & Grouping

Time Series Basics

Practical Data Analysis

Visualization Foundations

Basic Charts

Distribution & Comparison

Relationship Analysis

Storytelling & Dashboards

Smart Sales Insight Dashboard (Streamlit)

Customer Buying Behavior Analyzer

Business Performance Health Monitor

Marketing Campaign Impact Analyzer

Product Demand & Inventory Insight Tool

Machine Learning Basics

Data Preprocessing

Feature Scaling & Encoding

Regression Algorithms

Regression Metrics

Classification Algorithms

Classification Metrics

Clustering Algorithms

Projects

Deep Learning Basics

Activation Functions

Artificial Neural Network (ANN)

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

NLP Basics (GenAI Foundation)

Projects

Introduction to GenAI

Large Language Models (LLMs)

Setup & LangChain Basics

AI Agents

AI Agent Projects

RAG Systems

LangGraph & Advanced Workflows

MCP Servers