Program Details:

• Duration: 60–70 Days
• Level: Beginner to Intermediate
• Mode: Online / Hybrid

Module 1: Python for Data Science (7–8 Days)

• Python Basics: Syntax, Data Types, Control Flow
• Data Structures: Lists, Tuples, Sets, Dictionaries
• Functions, Lambda, Map, Filter
• NumPy Fundamentals
• Pandas for Data Analysis
• File Handling (CSV, Excel, JSON)
• Hands-on: Data Analysis Mini Project

Module 2: Data Analysis & Visualization (6–7 Days)

• Data Cleaning & Wrangling
• Exploratory Data Analysis (EDA)
• Visualization using Matplotlib & Seaborn
• AI-assisted EDA using LLMs
• Hands-on: Automated EDA using Generative AI

Module 3: Statistics for Data Science (6 Days)

• Descriptive Statistics
• Probability & Distributions
• Hypothesis Testing (Z-test, t-test, ANOVA)
• Hands-on: Statistical Analysis using Python

Module 4: SQL & BI Fundamentals (6 Days)

• SQL Queries & Joins
• Window Functions & CTEs
• Introduction to Power BI
• Data Modeling & Dashboards
• Hands-on: SQL Analysis & Power BI Dashboard

Module 5: Machine Learning Essentials (10 Days)

• Supervised Learning (Regression & Classification)
• Model Evaluation Metrics
• Ensemble Models
• Unsupervised Learning (Clustering, PCA)
• Hands-on: Machine Learning Project

Module 6: NLP Foundations for Generative AI (4 Days)

• Tokenization (Word, Subword – BPE, WordPiece)
• Stopwords, Stemming vs Lemmatization
• Tokens vs Words (LLM Context & Cost Relevance)
• Sentence Structure & POS Tagging (Conceptual)
• Dependency Relationships (High-Level)
• Word, Sentence & Document Embeddings
• Semantic Similarity & Cosine Distance
• Context Windows & Chunking Strategies
• Hands-on: Token Counting & Embedding Experiments

Module 7: Generative AI & LLMs (12 Days)

• Foundations of Generative AI
• Neural Networks & Transformers
• Prompt Engineering (Basic to Advanced)
• LLM APIs (OpenAI / Hugging Face)
• Text, Image & Code Generation
• Hands-on: Generative AI Applications

Module 8: LLM Applications & RAG (8 Days)

• LangChain Fundamentals
• Vector Databases & Embeddings
• Retrieval-Augmented Generation (RAG)
• AI Agents Basics
• Hands-on: Build RAG-Based Application

Module 9: Deployment & Capstone (7–8 Days)

• FastAPI for AI Applications
• Model Deployment Basics
• Capstone Project (End-to-End DS + GenAI)
• Resume & Interview Preparation

Program Outcomes

• Strong foundation in Data Science
• Clear understanding of NLP concepts powering LLMs
• Practical Generative AI & RAG skills
• Real-world AI deployment experience