| 1 |
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Lecture 1: What is Data Science? |
Lab 1: Intro to Python (numpy, graphing libraries, program structure, Jupyter Notebook) |
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R:HW0 |
| 2 |
Lecture 2: Data, Stats, and Visualization |
Lecture 3: Pandas and Scraping |
Lab 2: Python: sklearn, matplotlib |
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R:HW1 - D:HW0 |
| 3 |
Lecture 4: Introduction to Regression and kNN Regression |
Lecture 5: Linear Regression, Bootstrap and Confidence Intervals |
Lab 3: Scikit-learn for Simple Linear Regression |
Advanced Section 1: Linear Algebra and Hypothesis Testing |
R:HW2 - D:HW1 |
| 4 |
Lecture 6: Multi and Poly Regression |
Lecture 7: Model selection and Cross Validation |
Lab 4: Multiple Linear Regression and Cross Validation |
Advanced Section 2: Regularization Methods and Their Justifications |
R:HW3 - D:HW2 |
| 5 |
Lecture 8: Regularization and EDA |
Lecture 9: Visualization for Communication |
Lab 5: Matplotlib & Seaborn |
No Advanced Section |
No Assignment |
| 6 |
Lecture 10: kNN Classification & Logistic Regression I |
Lecture 11: Logistic Regression II |
Lab 6: Logistic Regression |
Advanced Section 3:Generalized Linear Models |
R:HW4 (individual) - D:HW3 |
| 7 |
No Lecture (Holiday) |
Lecture 12: Dealing with Missing Data, Imputation |
Lab 7: KNN Classification & Imputation |
No Advanced Section |
No Assignment |
| 8 |
Lecture 13: EthiCS |
Lecture 14: PCA |
Lab 8: PCA |
Advanced Sections 4: PCA |
R:HW5 - D:HW4 |
| 9 |
Lecture 15: Classification Trees |
Lecture 16: Bagging, & Random Forest |
Lab 9: Trees and Random Forests |
No Advanced Section |
R:HW6 - D:HW5 |
| 10 |
Lecture 17: Boosting Methods |
Lecture 18: Neural Networks 1 – Perceptron and MLP |
Lab 10: Boosting |
No Advanced Section |
No Assignment |
| 11 |
Lecture 19: NN 2: Anatomy of NN, design choices |
Lecture 20: NN 3. Back Propagation |
Lab 11: Intro to NN |
Advanced Sections 5: Decision Trees & Ensemble Methods |
R:HW7 (individual) - D:HW6 |
| 12 |
Lecture 21: Neural Networks 4. Regularization methods |
Lecture 22: Visualization for Model Interpretation |
Lab 12: Regularization with NN |
Advanced Sections 6: Solvers |
No Assignment |
| 13 |
Lecture 23: Experimental Design & Testing I |
No Lecture (Thanksgiving) |
No Lab |
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R:HW8 - D:HW7 [Due on Tuesday] |
| 14 |
Lecture 24: Experimental Design & Testing II |
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Lab 13: Web Dev for Final Projects |
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D:HW8 |
| 15 |
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Reading Period |
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| 16 |
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Finals Week |
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