| 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 |
|
|