Schedule



´╗┐Module Week Lecture Lecture Lab S-Section A-Section Homework Midterm
Module 0 1 Lecture 0: What is Data Science? (PP,KR) Lab 0: Intro to Python Homework 0
2 Labor Day (No Class) Lecture 1: Data; Stats; Visualization Lab 1: Python: Numpy, functions, Pandas, Matplotlib Homework 1
3 Lecture 2: Pandas and Scraping Lecture 3: Numpy; Scraping; Proper Visualization; EDA Lab 2: EDA S-Section 1: BeautifulSoup
Module 1 4 Lecture 4: Intro to Linear Regression and kNN Lecture 5: Multiple Regression and Bootstrap Lab 3: Linear Regression S-Section 2: Visualization A-Section 1 Homework 2
5 Lecture 6: Cross-Validation and Model Selection Lecture 7: Linear Model Regularization: Ridge & Lasso Lab 4: Model Selection S-Section 3 A-Section 2
6 Lecture 8: PCA and High Dimensionality; Dealing with Big Data Lecture 9: Visualization for Communication Lab 5: Regularization S-Section 4 A-Section 3 Homework 3 & Homework 4
Module 2 7 Columbus Day (No Class) Lecture 10: Logistic Regression I Lab 6: Logistic Regression & PCA S-Section 5 A-Section 4 Homework 5
8 Lecture 11: Logistic Regression II Lecture 12: kNN classification and dealing with missing data Lab 7: Logistic Regression & kNN Classification S-Section 6 A-Section 5 Homework 6 & Homework 7
9 Lecture 13: LDA and QDA Lecture 14: Classification Trees Lab 8: Discriminant Analysis & Classification Trees S-Section 7 A-Section 6
Module 3 10 Guest Lecture : Classification Summary; Ethics and Critical Thinking Guest Lecture: Storytelling with Data - Finding the Narrative in the Numbers Midterm
11 Lecture 15: Regression Trees and Random Forests Lecture 16: Boosting Lab 9: Random Forests and Boosting S-Section 8 A-Section 7 Homework 8
12 Lecture 17: Stacking Lecture 18: SVM I Lab 10: Projects S-Section 9
13 Lecture 19: SVM II Thanksgiving (No Class)
14 Lecture 20: AB Testing