Week Lecture (Mon) Lecture (Wed) Lab (Thu-Fri) Advanced Section (Wed) Assignment (R:Released Tue - D:Due Wed)
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: Scikit-learn 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 Cross-Validation 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: k-NN 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