Module 1: Introduction to Data Science
What is Data Science?
Data Science lifecycle
Roles: Data Analyst, Data Scientist, ML Engineer
Business use cases and applications

Module 2: Python for Data Science
Python fundamentals
Data structures and functions
NumPy, Pandas
Data cleaning and preprocessing

Module 3: Statistics & Probability
Descriptive statistics
Probability concepts
Distributions
Hypothesis testing
Correlation and regression basics

Module 4: Data Visualization
Data storytelling principles
Matplotlib, Seaborn
Exploratory Data Analysis (EDA)
Dashboards and reporting concepts

Module 5: Exploratory Data Analysis (EDA)
Data profiling
Handling missing values
Outlier detection
Feature understanding

Module 6: Machine Learning Fundamentals
Supervised vs unsupervised learning
Regression models
Classification algorithms
Model evaluation metrics

Module 7: Advanced Machine Learning
Decision trees and random forests
Clustering techniques
Dimensionality reduction
Model tuning and optimization

Module 8: SQL & Data Management
SQL basics and advanced queries
Joins, subqueries, indexing
Working with structured and unstructured data

Module 9: Big Data & Cloud Overview
Introduction to Big Data
Hadoop & Spark overview
Cloud platforms (AWS / Azure / GCP)
Data pipelines basics

Module 10: Data Science for Business
Business problem framing
KPI identification
Data-driven decision making
Ethics and data privacy

Module 11: Tools & Frameworks
Jupyter Notebook
Scikit-learn
Git & version control
Model deployment basics

Module 12: Capstone Project
End-to-end data science project
Real-world dataset
Model building and presentation