Installation and Setup

Lesson 1: Data Exploration and Cleaning

• Python and the Anaconda Package Management System
• Different Types of Data Science Problems
• Loading the Case Study Data with Jupyter and pandas
• Data Quality Assurance and Exploration
• Exploring the Financial History Features in the Dataset
• Activity 1: Exploring Remaining Financial Features in the Dataset

Lesson 2: Introduction to Scikit-Learn and Model Evaluation

• Introduction
• Model Performance Metrics for Binary Classification
• Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

Lesson 3: Details of Logistic Regression and Feature Exploration

• Introduction
• Examining the Relationships between Features and the Response
• Univariate Feature Selection: What It Does and Doesn't Do
• Building Cloud-Native Applications
• Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients

Lesson 4: The Bias-Variance Trade-off

• Introduction
• Estimating the Coefficients and Intercepts of Logistic Regression
• Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
• Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

Lesson 5: Decision Trees and Random Forests

• Introduction
• Decision trees
• Random Forests: Ensembles of Decision Trees
• Activity 5: Cross-Validation Grid Search with Random Forest

Lesson 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client

• Introduction
• Review of Modeling Results
• Dealing with Missing Data: Imputation Strategies
• Activity 6: Deriving Financial Insights
• Final Thoughts on Delivering the Predictive Model to the Client