Day 1

Introduction to Data Science

• Data Science Lifecycle
• AI vs ML vs Deep Learning
• Roles and Responsibilities
• Setting up Python Environment
• Installing VS Code
• Installing Python
• Virtual Environment
• Jupyter Notebook

Hands-on

• Install required software
• Write first Python program

Source: Python for Data Science

Day 2

Python Basics

• Variables
• Data Types
• Type Casting
• Input and Output
• Operators

Hands-on

• Small coding exercises

Day 3

Control Flow

• if
• elif
• else
• Nested Conditions
• Match Case

Hands-on

• Number guessing
• Grade calculator

Day 4

Loops

• For Loop
• While Loop
• Range
• Break
• Continue
• Pass

Hands-on

• Pattern programs
• Multiplication tables

Day 5

Functions

• Function Definition
• Parameters
• Return Values
• Lambda Functions
• Scope
• Recursion Introduction

Hands-on

• Calculator using functions

Day 1

Collections

• List
• Tuple
• Set
• Dictionary

Hands-on

• Student Marks Management

Day 2

String Handling

• String Methods
• Formatting
• Regular Expressions Introduction

Hands-on

• Text Analyzer

Day 3

File Handling

• Read Files
• Write Files
• CSV Files
• JSON Files

Hands-on

• Employee Database

Day 4

Exception Handling

• Try
• Except
• Finally
• Custom Exceptions

Hands-on

• Banking Application

Day 5

Object Oriented Programming

• Classes
• Objects
• Constructor
• Inheritance
• Polymorphism
• Encapsulation

Hands-on

• Library Management

Day 1

NumPy

• Arrays
• Indexing
• Slicing
• Broadcasting
• Vectorization

Hands-on

• Matrix Operations

Day 2

Pandas Basics

• Series
• DataFrame
• Import CSV
• Export CSV

Hands-on

• Employee Dataset

Day 3

Data Cleaning

• Missing Values
• Duplicate Records
• Data Types
• Rename Columns

Hands-on

• Cleaning Sales Dataset

Day 4

Data Transformation

• Filtering
• Sorting
• Merge
• Join
• GroupBy

Hands-on

• Retail Dataset

Day 5

Exploratory Data Analysis

• Summary Statistics
• Correlation
• Distribution
• Business Insights

Hands-on

• EDA Notebook

Day 1

Database Basics

• RDBMS
• MySQL
• PostgreSQL
• Tables

Day 2

SQL Queries

• SELECT
• WHERE
• ORDER BY
• LIMIT

Day 3

Aggregations

• GROUP BY
• HAVING
• Aggregate Functions

Day 4

JOIN Operations

• INNER
• LEFT
• RIGHT
• FULL
• SELF JOIN

Day 5

Advanced SQL

• CTE
• Window Functions
• Views
• Indexes

Source: SQL for Data Science

Day 1

Descriptive Statistics

Day 2

Probability

Day 3

Distributions

• Normal
• Binomial
• Poisson

Day 4

Hypothesis Testing

Day 5

Correlation and Regression Basics

Source: Statistics for Data Science

Day 1

Matplotlib

Day 2

Plotly

Day 3

Advanced Visualization

• Heatmaps
• Pairplots
• Distribution

Day 4

Dashboard Design Principles

Day 5

Storytelling with Data

Source: Data Visualization

Day 1

Machine Learning Workflow

Day 2

Regression

• Linear Regression

Day 3

Classification

• Logistic Regression

Day 4

Decision Trees, Random Forest

Day 5

KNN, Naive Bayes

Source: Machine Learning Fundamentals

Day 1

SVM

Day 2

Ensemble Learning

• Bagging
• Boosting

Day 3

Clustering

• K-Means

Day 4

PCA, Feature Selection

Day 5

Model Evaluation

• Accuracy
• Precision
• Recall
• F1
• ROC

Day 1

Neural Networks

Day 2

TensorFlow, Keras

Day 3

ANN

Day 4

CNN

Day 5

Transfer Learning

Source: Deep Learning

Day 1

Time Series Basics

Day 2

Forecasting

Day 3

Natural Language Processing

Day 4

Text Preprocessing

Day 5

Sentiment Analysis

Source: NLP

Day 1

Git, GitHub

Day 2

FastAPI

Day 3

Model Deployment

Day 4

Docker

Day 5

Project Structure, Logging, Environment Variables

Source: Git & GitHub

Source: Docker

Source: FastAPI

Day 1

Introduction to Large Language Models

• Evolution of AI
• Generative AI vs Traditional AI
• Transformers Overview
• Popular Foundation Models
• Prompt Engineering Basics

Hands-on

• Explore LLM capabilities using OpenAI or open-source models
• Design effective prompts for business scenarios

Day 2

Embeddings and Vector Databases

• Text Embeddings
• Similarity Search
• Vector Databases
• Semantic Search
• Introduction to FAISS and ChromaDB

Hands-on

• Generate embeddings
• Perform semantic similarity search

Day 3

Retrieval-Augmented Generation (RAG)

• RAG Architecture
• Document Loading
• Text Chunking
• Embedding Pipelines
• Retrieval Strategies

Hands-on

• Build a simple RAG pipeline using local documents

Day 4

AI Agents and Orchestration

• LangChain Fundamentals
• LangGraph Introduction
• Tool Calling
• Multi-step Workflows
• AI Agent Design Patterns

Hands-on

• Build a simple AI assistant capable of answering questions from uploaded documents

Day 5

Production AI Applications

• Responsible AI
• AI Safety and Hallucination Mitigation
• LLM Evaluation
• Cost Optimization
• AI Application Architecture
• End-to-End AI System Design

Hands-on

• Integrate an LLM into a FastAPI application

Source: Generative AI