Introduction to foundational statistics:

• Basic statistics
• Expected value / standard deviation / variance / covariance
• Statistical tests and significance
• Linear regression
• Single variable regression
• Multiple regression

Best practices for model building:

• Introduction to the model building process
• Splitting data into train/test/validation sets
• Multiple regression – dealing with correlated predictors
• Commercial applications of regression

Regression model evaluation:

• Distribution of errors: Q-Q plot, heteroscedasticity
• Multiple regression
• R2 and adjusted R2
• p-values and t-test
• F-test and F-distribution

Implementing clustering and unsupervised machine learning

• What is unsupervised machine learning?
• Introduction to clustering
• k-means clustering on multi-dimensional data
• Evaluating the quality of clustering Pitfalls of clustering

Introduction to classification:

• k-Nearest Neighbors
• Decision trees: gini coefficient and information gain
• Introduction to random forests
• Confusion matrices, misclassification rates
• Base line errors and lift

Pitfalls and best practices of data science:

• Understanding the limits of your data
• Checking data validity
• Ethical considerations
• Best practices for data analysts