🎓 50 Hours Data Science with Generative AI & LLMs (Python)

🔹 Course Objective

To build strong Data Science foundations and advance learners into Generative AI & Large Language Models (LLMs) with real-world projects using Python.

🕒 Course Breakdown (50 Hours)

Module 1: Python for Data Science (6 Hours)

• Python Basics: Syntax, Variables, Data Types

• Control Statements & Functions

• Data Structures: Lists, Tuples, Sets, Dictionaries

• NumPy: Arrays, Indexing, Broadcasting

• Pandas: DataFrames, Series, Data Operations

• Reading/Writing CSV, Excel, JSON

📌 Hands-on Lab: Data cleaning using Pandas

Module 2: Data Preprocessing & Feature Engineering (6 Hours)

• Handling Missing Values

• Removing Duplicates

• Outlier Detection & Treatment

• Encoding Categorical Variables

• Feature Scaling & Transformation

• Feature Selection Techniques

📌 Hands-on Lab: Preprocessing a real-world dataset

Module 3: Exploratory Data Analysis (EDA) (6 Hours)

• Importance of EDA

• Descriptive Statistics

• Univariate & Bivariate Analysis

• Distribution Analysis

• Correlation Analysis

• Visualizing Relationships

• Pandas Profiling & EDA Automation

📌 Hands-on Lab: EDA on business dataset

Module 4: Data Visualization (5 Hours)

• Visualization Principles

• Matplotlib: Basic & Advanced Charts

• Seaborn: Statistical Visualizations

• Plotly: Interactive Dashboards

• Categorical vs Numerical Visualization

• Multi-plot Layouts & Styling

📌 Hands-on Lab: Interactive data visualization project

Module 5: Statistics for Data Science (5 Hours)

• Descriptive vs Inferential Statistics

• Probability Concepts

• Hypothesis Testing

• Confidence Intervals

• A/B Testing

• Business Decision-Making using Statistics

📌 Hands-on Lab: A/B Testing case study

Module 6: Machine Learning with Python (8 Hours)

• ML Workflow & Terminology

• Supervised vs Unsupervised Learning

• Regression Algorithms

• Classification Algorithms

• Clustering Techniques

• Model Evaluation Metrics

• Cross Validation & Hyperparameter Tuning

📌 Hands-on Lab: End-to-end ML pipeline

Module 7: Introduction to Generative AI (4 Hours)

• What is Generative AI?

• Evolution of AI → ML → DL → GenAI

• Transformer Architecture (Conceptual)

• Use cases of GenAI in Industry

• Overview of LLMs (GPT, LLaMA, Gemini)

📌 Hands-on Demo: Using OpenAI APIs with Python

Module 8: Large Language Models (LLMs) with Python (6 Hours)

• Tokenization & Embeddings

• Prompt Engineering Techniques

• Text Generation, Summarization, Q&A

• LangChain Basics

• Building LLM Pipelines

• Vector Databases (FAISS / Chroma – concept + demo)

📌 Hands-on Project: Build a Question-Answering Bot

Module 9: Mini Projects & Capstone (4 Hours)

• Data Science Project (EDA + ML)

• GenAI Project (LLM-based Application)

• Resume-ready GitHub Project

• Best Practices & Deployment Overview

📌 Capstone Examples:

• Customer Churn Prediction

• AI Chatbot using LLM

• Resume Analyzer with GenAI

🧰 Tools & Technologies Covered

• Python, NumPy, Pandas

• Matplotlib, Seaborn, Plotly

• Scikit-learn

• OpenAI API / LLM APIs

• LangChain

• Jupyter Notebook

🎯 Learning Outcomes

By the end of this 50-hour program, learners will:

• Build complete Data Science pipelines

• Apply Machine Learning models to real problems

• Understand and implement Generative AI & LLMs

• Create portfolio-ready projects

• Be job-ready for Data Analyst / Data Scientist / GenAI roles

🏆 Ideal For

• Students & Fresh Graduates

• Working Professionals

• Data Analysts upgrading to AI

• Anyone aiming for AI & GenAI careers