1 

Lecture 0: What is Data Science? 
Lab 1: Introduction to Python and its Numerical Stack 

R:HW0 
2 
Lecture 1: Data, Summaries and Visuals 
Lecture 2: Data Engineering  The Grammar of Data 
Lab 2: Python for Data Collection and Cleaning 

R:HW1  D:HW0 
3 
Lecture 3: Effective Exploratory Data Analysis and Visualization 
Lecture 4: Linear Regression, kNN Regression and Inference 
Lab 3: Scikitlearn for Regression 
Advanced Section 1: Linear Algebra and Hypothesis Testing 
R:HW2  D:HW1 
4 
Lecture 5: Linear Regression, Confidence Intervals and Standard Errors 
Lecture 6: Multiple Linear Regression, Polynomial Regression and Model Selection 
Lab 4: Multiple Linear Regression and Polynomial Regression 
Advanced Section 2: Model Selection and Information Criteria 
R:HW3  D:HW2 
5 
Lecture 7: Regularization 
Lecture 8: High Dimensionality and Principal Component Analysis (PCA) 
Lab 5: Regularization and CrossValidation 
Advanced Section 3: Methods of Regularization and Justifications 
R:HW4(individual) D:HW3 
6 
No Lecture: Columbus Day 
Lecture 9: Visualization for Communication 
No Lab 
No Advanced Section 
No Assignment 
7 
Lecture 10: Logistic Regression 1 
Lecture 11: Logistic Regression 2 
Lab 6: Logistic Regression and Principal Component Analysis 
Advanced Section 4: Methods of Dimensionality Reduction  Principal Component Analysis 
R:HW5  D:HW4 
8 
Lecture 12: Artificial Neural Networks 1  Perceptron and Back Propagation 
Lecture 13: kNN for Classification and Dealing with Missingness 
Lab 7: NumPy for Building Artificial Neural Networks and Dealing with Missing Values 
Advanced Section 5: Generalized Linear Models, Logistic Regression and Beyond 
R:HW6  D:HW5 
9 
Lecture 14: Discriminant Analysis  Linear and Quadratic (LDA/QDA) 
Lecture 15: Classification Trees 
Lab 8: Discriminant Analysis [and Classification Trees] 
Advanced Section 6: Topics in Supervised Classification 
R:HW7  D:HW6 
10 
Lecture 16: Regression Trees, Bagging and Random Forest 
Lecture 17: Boosting Methods 
Lab 9: Decision Trees, Bagged Trees, Random Forests and Boosting 
Advanced Section 7: Decision Trees and Ensemble Methods 
R:HW8  D:HW7 
11 
Lecture 18: Artificial Neural Networks 2  Anatomy of ANN 
Lecture 19: Artificial Neural Networks 3  Regularization Methods for ANN 
Lab 10: Keras for Artificial Neural Network 
Advanced Section 8: Artificial Neural Networks for Image Analysis 
R:HW9(individual) D:HW8 
12 
Lecture 20: Support Vector Machines 
No Lecture: Thanksgiving 
No Lab 
No Advanced Section 
No Assignment 
13 
Lecture 21: Stacking 
Lecture 22: Responsible Data Science  Guest Lecture (Julia Stoyanovich) 
No Lab 
Advanced Section 9: Support Vector Machines 
D:HW9 
14 
Lecture 23: A/B Testing 
Lecture 24: Final Lecture 



15 


Reading Period 


16 


Finals Week 

