Data Science Projects with Python Course Syllabus

Overview: This project-driven course guides you through the complete data science workflow using Python, from data cleaning and exploration to model deployment and business impact analysis. With over 24 hours of hands-on learning, you'll work on real-world datasets across seven projects, all within an interactive browser environment. Each module builds practical skills using industry-standard tools like pandas, scikit-learn, XGBoost, and SHAP, culminating in client-ready deliverables that demonstrate real-world impact.

Module 1: Introduction

Estimated time: 0.5 hours

• Role of machine learning in data science
• Essential Python libraries: pandas, scikit-learn, Matplotlib
• Set up in Jupyter environment
• Load case-study data and verify data integrity

Module 2: Data Exploration & Cleaning

Estimated time: 4 hours

• Perform data-quality checks
• Handle missing values and outliers
• Encode categorical variables
• Conduct exploratory data analysis on credit dataset

Module 3: Introduction to scikit-learn & Model Evaluation

Estimated time: 3.5 hours

• Generate synthetic data for testing
• Split data into training and test sets
• Train logistic regression models
• Evaluate models using accuracy and ROC curves

Module 4: Details of Logistic Regression & Feature Extraction

Estimated time: 4 hours

• Analyze feature-response relationships
• Apply univariate feature selection (F-test)
• Interpret logistic regression coefficients
• Plot decision boundaries and sigmoid function

Module 5: The Bias-Variance Trade-Off

Estimated time: 3.5 hours

• Understand gradient descent optimization
• Apply L1 and L2 regularization
• Implement cross-validation pipelines
• Perform hyperparameter tuning

Module 6: Decision Trees & Random Forests

Estimated time: 3.25 hours

• Train decision tree classifiers
• Measure node impurity and tree depth
• Use ensemble methods with random forests
• Optimize models via grid search

Module 7: Gradient Boosting, XGBoost & SHAP Values

Estimated time: 3 hours

• Configure XGBoost hyperparameters
• Apply learning rate and early stopping
• Perform randomized grid search
• Interpret model outputs using SHAP values

Module 8: Test-Set Analysis, Financial Insights & Delivery

Estimated time: 2.5 hours

• Calibrate prediction probabilities
• Generate decile cost charts
• Derive financial metrics: cost savings, ROI
• Prepare client-ready model deliverables

Prerequisites

• Familiarity with basic Python syntax
• Understanding of fundamental statistics
• No prior machine learning experience required

What You'll Be Able to Do After

• Explore and clean real-world datasets using pandas
• Build and evaluate logistic regression and tree-based models
• Apply advanced techniques like XGBoost and SHAP for performance and interpretability
• Conduct business-impact analysis and deliver actionable insights
• Create production-ready data science deliverables