Data Mining for Business Analytics

This course covers data mining methods for business analytics. It is an entry-level course for undergraduate students in the business school to learn advanced techniques in data mining with hands-on experience. It also introduces generative AI tools to help students effectively apply data analytics with the support of generative AI. If any page or link cannot be loaded, please refer to Blackboard for the materials.

Lecture and Lab Notes

Introduction to Data Mining for Business Analytics

Lecture Notes	Labs
1-1. Data Mining 101
1-2. Introduction to R	Lab1-R-Basic
1-3. Introduction to RMarkdown
1-4. Advanced techniques: function and loop	1-4: function and loop

Mondern Data Wrangling

Notes	Labs
2-1. Modern Data Wrangling	2-1. Data Wrangling Examples
2-2. dplyr package

Advanced Visualization Tools

Notes	Labs
3-1. Advanced Visualization Tools: ggplot2 & plotly

AI Adoption in Data Analytics

Notes	Labs
4-1. Intro to Generative AI for Business
4-2. AI Adoption in Data Analytics
4-3. Prompt Engineering

Cross-validation and Performance Evaluation

Notes	Labs
5-1. Intro to Cross-validation
5-2. Performance Evaluation

Prediction and Classification Methods

Notes	Labs
6-1. Classical Regression & Classification Methods
6-2. Classification and Regression Tree (CART)

Clustering Analysis

Notes	Labs
7-1. Clustering Analysis

Neural Networks

Notes	Labs
8-1. Neural Network Models

Time-series forecasting

Notes	Labs
9-1. Time-series forecasting

Basic Text Mining

Notes	Labs
10-1. Basic Text Mining