This repository hosts online learning materials for undergraduate and graduate Data Mining / Business Analytics courses taught by Dr. Zhaohu (Jonathan) Fan.
The notes are written in Python and are designed to support:
- Business analytics and data science learners
- Students in data mining, machine learning, and predictive analytics courses
- Practitioners looking for applied, example-driven materials
Contributors:
- Zhaohu(Jonathan) Fan, Ph.D. in Business Analytics, psujohnny@gmail.com.
- Amin Aminimehr, Ph.D. student in Business Analytics.
- Harsh Singal, M.S. in Business Analytics (current position: Data Scientist - Product Analytics at Asurion).
| Description | |
|---|---|
| 1.A | Introduction to Data Mining |
| 1.B | Introduction to Python |
| 1.C | Advanced techniques: function and loop |
| 1.D | Introduction to Markdown (optional) |
| Description | |
|---|---|
| 2.A | Explore and describe dataset |
| 2.B | Exploratory data analysis by visualization |
| Description | |
|---|---|
| 3.A | Linear regression and prediction |
| 3.B | Subset variable selection |
| 3.C | LASSO variable selection |
| 3.D | Monte Carlo simulation |
| Description | |
|---|---|
| 4.A | Logistic regression and prediction |
| 4.B | Logistic regression and variable selection |
| 4.C | Logistic Regression for binary classification |
| 4.D | Logistic regression and ROC |
| Description | |
|---|---|
| 5.A | Cross validation |
| 5.B | Cross validation (Logit model) |
| Description | |
|---|---|
| 6.A | Regression Trees |
| 6.B | Classification Trees |
| Description | |
|---|---|
| 7.A | Bagging trees |
| 7.B | Random forests |
| 7.C | Boosting trees |
| Description | |
|---|---|
| 8.A | Univariate Nonparametric Smoothing |
| 8.B | Generalized additive model (GAM) |
| Description | |
|---|---|
| 9.A | Neural network models |
| 9.B | Neural network models (Handwritten Digits Case) |
| 9.C | Discriminant analysis (Optional) |
| 9.D | Support vector machine (SVM) (Optional) |
| Description | |
|---|---|
| 10.A | Clustering |
| Description | |
|---|---|
| 11.A | Association Rules |
| Description | |
|---|---|
| 12.A | Basic Text Mining |
Acknowledgments: I have drawn ideas or readings from the following texts:
- Ethan Swan, Python for Data Science
- And many more.