| Module 0 |
1 |
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Lecture 0: What is Data Science? (PP,KR) |
Lab 0: Intro to Python |
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Homework 0 |
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2 |
Labor Day (No Class) |
Lecture 1: Data; Stats; Visualization |
Lab 1: Python: Numpy, functions, Pandas, Matplotlib |
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Homework 1 |
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3 |
Lecture 2: Pandas and Scraping |
Lecture 3: Numpy; Scraping; Proper Visualization; EDA |
Lab 2: EDA |
S-Section 1: BeautifulSoup |
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| 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 |
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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 |
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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 |
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| 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 |
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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 |
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9 |
Lecture 13: LDA and QDA |
Lecture 14: Classification Trees |
Lab 8: Discriminant Analysis & Classification Trees |
S-Section 7 |
A-Section 6 |
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| Module 3 |
10 |
Guest Lecture : Classification Summary; Ethics and Critical Thinking |
Guest Lecture: Storytelling with Data - Finding the Narrative in the Numbers |
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Midterm |
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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 |
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12 |
Lecture 17: Stacking |
Lecture 18: SVM I |
Lab 10: Projects |
S-Section 9 |
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13 |
Lecture 19: SVM II |
Thanksgiving (No Class) |
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14 |
Lecture 20: AB Testing |
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