Module 0. About the Course (2 min)

Module 1. The Modeling Process (30 min)

• The Data Science Process
• Types of Data Science Projects
• Modeling
• Project Type and Maturity
• Data Science Starting Point
• Other Modeling Considerations

Module 2. Overview of Common Algorithms and Uses (68 min)

• Data Science Framework
• Approaches
• Techniques
• Algorithms
• Anomaly Detection

• Evaluation
• Bias/Variance Tradeoff
• Train and Test Sets
• Assessment of Results
• Hold-out Cross Validation
• K-fold Cross Validation Method
• Regression – Mean Squared Error
• Linear Regression Confidence and Prediction Intervals
• Logistic Regression – Significance Test
• Classification Accuracy
• Classification Accuracy – Other Measures
• Prediction Error Methods
• ROC Curve
• Evaluation – Customer Acceptance

Module 4. Preparing for Deployment (11 min)

• Deployment
• Deployment – Working Software
• Data Pipelines
• Data Pipelines – Part of Deployment: Part 1 – 2
• Deployment Operationalization
• Monitoring Models – Dashboard
• Life of the Model

Module 5. Model Operations (18 min)

• Model Operations: Part 1 – 2
• Data Ingestion
• Data Storage
• Data Integration and Synthesis
• Data Visualization
• Model Accuracy
• Model Retraining
• Model Retiring
• Machine Learning in Action

Module 6. Model Metrics (29 min)

• Machine Learning Metrics
• Metrics for Supervised Learning
• Classification Model Metrics
• Normalized Discount Cumulative Gain (NDCG)
• Discount Cumulative Gain (DCG)
• Normalized Discount Cumulative Gain (NDCG) – Example
• Root Mean Squared Error
• Quantities of Error