
Advanced Data Analytics for Organizations
- 1: Course Intro
- 2: Under and Overfitting
- 3: Anomaly/Outlier Detection
- 4: Python Modeling
- 5: CART: Classification and Regression Trees
- 6: MLR: Multiple Linear Regression
- 7: Related Input Variables and Related Output Variables
- 8: TSF: Time Series Forecasting
- 9: Categorical Prediction Model Evaluation
- 10: LogReg: Logistic Regression
- 11: KNN: K-Nearest Neighbors
- 12: Midterm Exam
- 13: ANN: Artificial Neural Networks
- 14: GB: Gradient Boosting
- 15: RF: Random Forests
- 16: Cluster Analysis and Subpopulations
- 17: SVM: Support Vector Machines and Profit Analysis
- 18: Project: Putting It All Together
- 19: Final Exam
- 20: Foundation Stats (Resource)
- 21: Python Basics (Resource)
- 22: Missing Values (Resource)
- 23: Data Management and Sampling (Resource)
- 24: Changing the Probability Cutoff