lectures
- Lecture 0: What is Data Science? (Sep. 05, 2018)
- Lecture 1: Data, Summaries and Visuals (Sep. 10, 2018)
- Lecture 2: Data Engineering - The Grammar of Data (Sep. 12, 2018)
- Lecture 3: Effective Exploratory Data Analysis and Visualization (Sep. 17, 2018)
- Lecture 4: Linear Regression, kNN Regression and Inference (Sep. 19, 2018)
- Lecture 5: Linear Regression, Confidence Intervals and Standard Errors (Sep. 24, 2018)
- Lecture 6: Multiple Linear Regression, Polynomial Regression and Model Selection (Sep. 26, 2018)
- Lecture 7: Regularization (Oct. 01, 2018)
- Lecture 8: High Dimensionality and Principal Component Analysis (PCA) (Oct. 03, 2018)
- Lecture 9: Visualization for Communication (Oct. 10, 2018)
- Lecture 10: Logistic Regression 1 (Oct. 15, 2018)
- Lecture 11: Logistic Regression 2 (Oct. 17, 2018)
- Lecture 12: Artificial Neural Networks 1 - Perceptron and Back Propagation (Oct. 22, 2018)
- Lecture 13: k-NN for Classification and Dealing with Missingness (Oct. 24, 2018)
- Lecture 14: Discriminant Analysis - Linear and Quadratic (LDA/QDA) (Oct. 29, 2018)
- Lecture 15: Classification Trees (Oct. 31, 2018)
- Lecture 16: Regression Trees, Bagging and Random Forest (Nov. 05, 2018)
- Lecture 17: Boosting Methods (Nov. 07, 2018)
- Lecture 18: Artificial Neural Networks 2 - Anatomy of ANN (Nov. 12, 2018)
- Lecture 19: Artificial Neural Networks 3 - Regularization Methods for ANN (Nov. 14, 2018)
- Lecture 20: Support Vector Machine (SVM) (Nov. 19, 2018)
- Lecture 21: Stacking (Nov. 26, 2018)
- Lecture 22: Responsible Data Science (Nov. 28, 2018)
- Lecture 23: A/B Testing (Dec. 03, 2018)
- Lecture 24: Final Lecture (Dec. 05, 2018)