Python Basics & Environment Setup

• Course overview, tools installation (Anaconda / Jupyter / Git)
• Python syntax, variables, data types
• Conditional statements, loops, and functions
  
  Hands-on
• Coding exercises on control flow & loops
• Mini Project: Text counter / simple calculator / number guessing game

Python Data Structures & Libraries

• Lists, tuples, sets, dictionaries
• String manipulation
• Introduction to NumPy: arrays, slicing, vectorized operations
  
  Hands-on
• Practice exercises on lists and dictionaries
• NumPy hands-on: matrix operations, basic statistics

Data Handling and Analysis with Pandas

• DataFrames, reading/writing CSV, Excel
• Filtering, grouping, and merging
• Handling missing values and data cleaning basics
  
  Hands-on
• EDA (Exploratory Data Analysis) on a dataset (Titanic or Sales data)
• Pandas practice challenge

Data Visualization with Python

• Basics of Matplotlib and Seaborn
• Charts: line, bar, histogram, boxplot, scatter
• Visual storytelling & identifying insights
  
  Hands-on
• Visualization lab: create 5 types of plots from a dataset
• Mini visualization project on Iris / Sales data

SQL Fundamentals

• Introduction to SQL & RDBMS concepts
• Writing basic queries: SELECT, WHERE, ORDER BY, LIMIT
• Aggregations: COUNT, SUM, AVG, GROUP BY, HAVING
  
  Hands-on
• SQL lab: run queries on SQLite/MySQL sample database
• Exercise set: Analyze an employee/sales dataset

Intermediate SQL for Data Analysis

• Joins: INNER, LEFT, RIGHT, FULL
• Subqueries, CTEs, and window functions
• Combining SQL with Python (sqlite3 / SQLAlchemy)
  
  Mini Project
• SQL analytical exercises (customer behavior, sales trends)
• Mini project: E-commerce analytics dashboard (SQL + Pandas)

Introduction to Machine Learning

• ML concepts: Supervised vs Unsupervised Learning
• ML workflow: data → training → evaluation
• Introduction to scikit-learn, train-test split, metrics
  
  Mini Project
• Build first ML model (Decision Tree / KNN) on Iris dataset
• Evaluate using confusion matrix and accuracy metrics

Regression Models

• Linear Regression: intuition, assumptions, metrics
• Polynomial regression and overfitting
• Regularization (Ridge, Lasso) concepts
  
  Hands-on
• Hands-on: Predict housing prices (Boston dataset)
• Model evaluation with RMSE, R²

Classification Models

• Logistic Regression, KNN, Decision Trees, Random Forest
• Model evaluation: precision, recall, F1, ROC-AUC
  
  Hands-on
• Classification lab: Titanic / Customer churn prediction
• Model tuning using GridSearchCV

Unsupervised Learning & Feature Engineering

• Clustering (K-Means, Hierarchical)
• PCA for dimensionality reduction
• Feature scaling and encoding categorical data
  
  Hands-on
• Customer segmentation using K-Means

Model Optimization & Deployment

• End-to-end ML pipeline (preprocessing → model → evaluation)
• Saving/loading models (pickle, joblib)
• Simple deployment using Streamlit or Flask
  
  Hands-on
• Build a Streamlit ML app (e.g., Predict house price)
• Integrate model + interfacey

Capstone Project & Deployment

Build and present end-to-end Project

• Capstone briefing, project setup
• Project discussion, dataset selection, and approach
• Guidance on storytelling, visualization, and presentation
• Students implement full end-to-end project
• Final project presentation and feedback session