Course Description
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.
- No. of credits: 3(3,0)
-
Time allocation:
- Theory (hours): 30
- Practice (hours): 15
- Self-study (hours): 90
-
Course contents:
- This course is broken down into 15 modules designed to provide the student with an overview and details of business intelligence and data mining, together with hands-on practices. Each module contains a prescribed reading, an assignment, and a quiz.
- This course uses weekly sessions to enrich the course and promote interaction as a vital skill in improved idea creation, analysis, and decision-making.
Textbook
- Wilfried Grossmann, Stefanie Rinderle-Ma. Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg, 2015.
- John D. Kelleher, Brian Mac Namee, Aoife D'Arcy. Fundamentals of Machine Learning for Predictive Data Analytics: algorithms, worked examples, and case studies. MIT press, 2020.
- Joshua N. Milligan. Learning Tableau 2020, Fourth Edition. Packt Publishing, 2020.
- Tom Mitchell. Machine Learning. McGraw Hill, 1997.
- Carlo Vercellis. Business Intelligence: Data Mining and Optimization for Decision Making. New York: Wiley, 2009.
- Google - Machine Learning Crash Course with TensorFlow APIs
Evaluation
| Evaluation categories | Weight (%) | Types |
|---|---|---|
| Process evaluation 1 | 10 | Process Exercise |
| Process evaluation 2 | 20 | Essay |
| Mid-term project | 20 | Presentation |
| Final project | 50 | Report |
Schedule
| Group | Day [Period] | e-Learning | Room |
|---|---|---|---|
| N1 (TC) | Monday [2] | | | | D.405 |
| N4 (CLC) | Wednesday [2] | | | | E.505 |
Syllabus
| 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 |
Chapter 5: Decision Tree Learning - Part 2: Regression — Read 5: Decision Tree Learning |
ch05.pdf r05.pdf |
| 8 |
Hands-on Practice: - (7) Making Visualizations That Look Great and Work Well - (8) Telling a Data Story with Dashboards |
hp07.pdf hp08.pdf hp-rs-07.zip hp-rs-08.zip |
| 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.
Resources
- Tableau Desktop 2023 (macOS & Windows 64-bit) [Mirror-1 | Mirror-2]
- Tableau Hands-on Practice Resources
- Danh Sách Nhóm & Lịch Báo Cáo (HK1/23-24)
- Danh Sách Sinh Viên (HK1/23-24)
- Attendance records:
Staffs
-
Lecturer: Phuc H. Duong
- Email: duonghuuphuc@tdtu.edu.vn
- Website: www.duonghuuphuc.com
- Student assistants: Kuo Nhan Dung, Le Gia Phu, Tran Khai Hoang
Policies
- You are allowed to absent up to 3 sessions of lecture hours.
- Exercises, assignment and final project must be submitted by the due date. No late submission will be accepted.
- For assignment and final project, all members of group must submit the work together.
- About collaboration, you may discuss with other students on the review reports. However, you must write up the reports on your own independently.
- You need to be honest in all academic work and understanding that failure to comply with this commitment will result in disciplinary action.
- For online class sections (if any), attendance and participation are determined by active interaction in the weekly discussion forums and submission of assignments. Failure to complete at least 50% of the work each week will be deemed as lack of active participation in the course.
Contact
- Office: Room C.119, TDTU Campus (Tan Phong, HCMC)
- Personal email: dhp@fastai.dev
Archived
