📍 Module 1: Introduction to Data Science

• What is Data Science?

• Data Science Workflow & Lifecycle

• Roles: Data Analyst vs Data Scientist vs ML Engineer

• Tools & Technologies in Demand

• Real-World Use Cases

📍 Module 2: Python for Data Science

• Python Basics: Data types, Loops, Functions

• Working with NumPy and Pandas

• Data Cleaning, Wrangling, and Transformation

• Exploratory Data Analysis (EDA)

• Visualization using Matplotlib & Seaborn

📍 Module 3: Statistics & Probability for Data Science

• Descriptive & Inferential Statistics

• Probability Distributions

• Hypothesis Testing

• Sampling Methods

• Correlation vs Causation

📍 Module 4: SQL for Data Science

• RDBMS Concepts

• SQL Basics: SELECT, WHERE, GROUP BY

• JOINS, Subqueries, Window Functions

• Data Aggregation & Analysis Queries

• Case Studies Using SQL

📍 Module 5: Data Visualization & Dashboarding

• Data Visualization Principles

• Plotting with Python: Seaborn, Plotly

• Creating Dashboards with Power BI or Tableau

• Storytelling with Data

• Visual Analysis of Business KPIs

📍 Module 6: Machine Learning with Python

• Supervised vs. Unsupervised Learning

• Regression: Linear, Logistic

• Classification: KNN, Decision Trees, Random Forest

• Clustering: K-Means

• Model Evaluation: Accuracy, Confusion Matrix, ROC-AUC

📍 Module 7: Advanced ML Concepts

• Ensemble Methods: Bagging, Boosting (XGBoost)

• Feature Engineering Techniques

• Dimensionality Reduction (PCA)

• Model Tuning (Cross-Validation, Grid Search)

• Bias-Variance Tradeoff

📍 Module 8: Introduction to Deep Learning

• Understanding Neural Networks

• Activation Functions & Optimizers

• Building Neural Networks using Keras & TensorFlow

• CNN & RNN Overview

• Deep Learning Applications in Vision & NLP

📍 Module 9: Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, Lemmatization

• Word Embeddings: Word2Vec, TF-IDF

• Sentiment Analysis

• Text Classification

• Chatbot Development Basics

📍 Module 10: Model Deployment & Projects

• Model Deployment with Streamlit or Flask

• Creating APIs for ML Models

• Hosting Projects on GitHub

• End-to-End Project: Data Cleaning → Model → Deployment

• Documentation & Presentation