Module 1: Python Programming Fundamentals 

• Python essentials: variables, data types, control flow, functions

• Working with lists, dictionaries, sets and comprehensions

• File handling, modules and virtual environments

• Writing clean, readable, debuggable Python

• Setting up your data science workspace (Jupyter / VS Code)

Module 2: Data Handling with Pandas & NumPy

• NumPy arrays, vectorized operations and broadcasting

• Pandas Series and DataFrames — the workhorses of data analysis

• Loading CSV, Excel and JSON datasets

• Selecting, filtering, sorting and aggregating data

• Group-by, pivot tables and merging datasets

Module 3: Data Cleaning & Preparation 

• Handling missing values the right way

• Detecting and treating outliers

• Type conversions, encoding and scaling

• Working with dates, strings and categorical data

• Hands-on: Mini Project — Data Analysis Task on a real dataset

Module 4: Exploratory Data Analysis (EDA) 

• The EDA mindset: question the data before modelling

• Univariate, bivariate and multivariate analysis

• Correlation, distributions and statistical summaries

• Spotting data quality issues and biases

• Translating EDA findings into clear business insights

Module 5: Data Visualization Techniques 

• Matplotlib essentials and customisation

• Seaborn for fast, beautiful statistical plots

• Choosing the right chart for the question (bar, line, scatter, heatmap, box)

• Designing visuals that tell a story, not just display data

• Interactive visualisations and dashboard basics

Module 6: Real-World Dataset Analysis 

• Sourcing and loading real public datasets

• Building a structured analysis workflow

• Drawing actionable insights from messy data

• Presenting results to a non-technical audience

• Hands-on Project: Insight Generation Report

Module 7: Supervised Learning Foundations 

• What ML is — and what it isn't

• Regression vs Classification — when to use which

• Train / validation / test splits and why they matter

• Bias-variance trade-off in plain language

• Linear & logistic regression, decision trees, k-NN

Module 8: Model Building with Scikit-learn 

• The Scikit-learn API: fit, predict, score

• Feature engineering and feature selection

• Pipelines and reproducible workflows

• Hyperparameter tuning basics

• Avoiding data leakage

Module 9: Model Evaluation & Optimization 

• Regression metrics: MAE, MSE, RMSE, R²

• Classification metrics: accuracy, precision, recall, F1, ROC-AUC

• Cross-validation and robust performance estimation

• Confusion matrices and error analysis

• Hands-on: Mini ML Project (Regression or Classification)

Module 10: End-to-End ML Project Build 

• Choose your capstone: House Price Prediction, Spam Detection or Student Performance Predictor

• Project scoping, dataset selection and data understanding

• Building the full pipeline — clean → explore → model → evaluate

• Iterating with feedback from your mentor

• Documenting your decisions and trade-offs

Module 11: Deployment, Demo Day & Portfolio 

• Packaging your project for sharing and submission

• Pushing your work to GitHub with a strong README and clean notebooks

• Recording a clean walkthrough demo of your project

• Final project presentation to instructors and peers

• Adding the project to your resume, LinkedIn and portfolio site

Module 12: Hands-on Project