This course is designed to introduce students to business intelligence concepts and provide students with an understanding of data mining techniques, mathematical models along with business intelligence tasks, e.g., regression, time series, classification, association rules, clustering. We also provide some business intelligence applications in reality. Practical experience will be gained by practicing hands-on tutorials with leading BI software (Tableau). The objectives of this course are as follows: (i) student will define the importance of business intelligence; (ii) student will understand and be able to apply data mining techniques in various business intelligence tasks; (iii) student will understand how to use the Tableau software.
| Evaluation categories | Weight (%) | Types |
|---|---|---|
| Process evaluation 1 | 10 | Process Exercise |
| Process evaluation 2 | 20 | Essay |
| Mid-term project | 20 | Presentation |
| Final project | 50 | Report |
| Group | Day [Period] | e-Learning | Room |
|---|---|---|---|
| N1 (TC) | Monday [2] | | | | D.405 |
| N4 (CLC) | Wednesday [2] | | | | E.505 |
| Weeks | Topics | Resources |
|---|---|---|
| 1 |
Chapter 0: Course Introduction Chapter 1: Definition of Business Intelligence — Read 1: Definition of Business Intelligence |
ch00.pdf ch01.pdf r01.pdf |
| 2 |
Chapter 2: Modeling in Business Intelligence — Hands-on Practice: - (1) Taking Off with Tableau - (2) Connecting to Data in Tableau — Read 2: Modeling in Business Intelligence |
ch02.pdf hp01.pdf hp02.pdf hp-rs-01.zip hp-rs-02.zip superstore.zip r02.pdf |
| 3 |
Hands-on Practice: - (3) Moving Beyond Basic Visualizations - (4) Starting an Adventure with Calculations and Parameters |
hp03.pdf hp04.pdf hp-rs-03.zip hp-rs-04.zip |
| 4 |
Chapter 3: Data Provisioning — Read 3: Data Provisioning |
ch03.pdf r03.pdf |
| 5 |
Hands-on Practice: - (5) Leveraging Level of Detail Calculations - (6) Diving Deep with Table Calculations |
hp05.pdf hp06.pdf hp-rs-05.zip hp-rs-06.zip LOD-whitepaper.pdf |
| 6 |
Chapter 4: Decision Tree Learning - Part 1: Classification — Read 4: Decision Tree Learning |
ch04.pdf r04.pdf |
| 7 |
Hands-on Practice: - (7) Making Visualizations That Look Great and Work Well - (8) Telling a Data Story with Dashboards |
hp07.pdf hp08.pdf |
| 8 |
Chapter 5: Decision Tree Learning - Part 2: Regression — Read 5: Decision Tree Learning |
ch05.pdf r05.pdf |
| 9 |
Chapter 6: Bayesian Learning — Read 6: Bayesian Learning — Self-study 1: Data Description and Visualization |
ch06.pdf r06.pdf s01.pdf |
| 10 |
Chapter 7: Data Mining for Cross-Sectional Data — Read 7: Data Mining for Cross-Sectional Data |
ch07.pdf r07.pdf |
| 11 |
Hands-on Practice: - (9) Visual Analytics - Trends, Clustering, Distributions, and Forecasting — Self-study 2: Data Mining for Temporal Data |
hp09.pdf s02.pdf |
| 12 |
Hands-on Practice: - (10) Advanced Visualizations - (11) Dynamic Dashboards — Self-study 3: Process Analysis |
hp10.pdf hp11.pdf s03.pdf |
| 13 |
Mid-term Report — Self-study 4: Analysis of Multiple Business Perspectives |
s04.pdf |
| 14 |
Final Project Report
— Self-study 5: A Survey of Business Intelligence Tools |
s05.pdf |
| 15 | Final Project Report | — |
Use your student email account to access the above resources. Abbreviations: ch, hp, hp-rs, r, and s stand for lecture notes, hands-on practice tutorials, hands-on practice resources, reading materials, and self-study materials, respectively.