📣 Lịch báo cáo Giữa kỳ & Đồ án môn học đã được đăng tại trang Thông Báo.
Course Overview
This course provides a comprehensive introduction to Business Intelligence (BI) systems. It covers the principles and practices that organizations use to collect, manage, analyze, and visualize data for decision-making purposes. Additionally, the course explores emerging trends in BI, including the role of Generative AI in enhancing data-driven strategies.
Course Objectives — By the end of the course, students will:
- Understand the fundamental concepts of Business Intelligence.
- Learn how to design and implement BI solutions using various tools and techniques.
- Gain hands-on experience with Tableau software, training prediction models, and data visualization.
- Explore the impact of emerging technologies like Generative AI on BI.
- Develop and present a comprehensive BI project.
Course Info
- No. of credits: 3(3,0)
-
Time allocation:
- Theory (hours): 30
- Practice (hours): 15
- Self-study (hours): 90
Textbook
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 |
|---|---|---|---|
| N2 (TC) | Monday [2] | | | | B.405 |
| N3 (CLC) | Monday [3] | | | | B.301 |
| N1 (CLC-EN) | Tuesday [2] | | | | F.512 |
Syllabus
| Weeks | Topics | Resources |
|---|---|---|
| 1 |
Chapter 0: Course Introduction Chapter 1: Introduction to Business Intelligence — Read 0: Approaches for Conducting the Final Project Read 1: Sample Final Project Topic Read 2: KMS Use Case: Pharmaceutical Company |
ch00.pdf ch01.pdf — r00.pdf r01.pdf r02.pdf |
| 2 |
Chapter 1: Introduction to Business Intelligence (cont.) — Read 3: Starbucks' Use of BI to Optimize Menu and Store Locations Read 4: Leveraging GenAI for BI in E-Commerce Read 5: E-commerce Business Using Analytics to Enhance Customer Experience Read 6: Healthcare Provider Leveraging Analytics to Improve Patient Care |
ch01.pdf — r03.pdf r04.pdf r05.pdf r06.pdf |
| 3 |
Chapter 2: Descriptive Analytics (Part 1): Nature of Data, Statistical Modeling, and Visualization — Read 7: Retail Chain's Data Management Challenges Read 8: GIGO in Retail Demand Forecasting — Hands-on Practice: - (1) Taking Off with Tableau - (2) Connecting to Data in Tableau |
ch02.pdf — r07.pdf r08.pdf — hp01.pdf hp02.pdf hp-rs-01.zip hp-rs-02.zip superstore.zip |
| 4 |
Chapter 3: Decision Tree Learning — Part 1: Classification — Hands-on Practice: - (3) Moving Beyond Basic Visualizations - (4) Starting an Adventure with Calculations and Parameters — Read 9: Decision Tree Learning |
ch03.pdf — hp03.pdf hp04.pdf hp-rs-03.zip hp-rs-04.zip — r09.pdf |
| 5 |
Chapter 4: Data Visualization and Reporting — Hands-on Practice: - (5) Leveraging Level of Detail Calculations - (6) Diving Deep with Table Calculations |
ch04.pdf — hp05.pdf hp06.pdf hp-rs-05.zip hp-rs-06.zip |
| 6 |
Chapter 5: BI Tools and Technologies |
ch05.pdf |
| 7 |
Chapter 6: Advanced Analytics in BI Appendix B: Decision Tree Learning - Part 2: Regression — Read 10: Decision Tree Learning |
ch06.pdf apd-B.pdf — r10.pdf |
| 8 |
Chapter 7: Introduction to Big Data in BI — Hands-on Practice: - (7) Making Visualizations That Look Great and Work Well - (8) Telling a Data Story with Dashboards |
ch07.pdf — hp07.pdf hp08.pdf hp-rs-07.zip hp-rs-08.zip |
| 9 |
Chapter 8: Generative AI and its Role in BI Appendix C: Bayesian Learning — Read 11: Bayesian Learning — Self-study 1: Data Description and Visualization |
ch08.pdf apd-C.pdf — r11.pdf s01.pdf |
| 10 |
Chapter 9: Real-time BI and Emerging Trends — Read 12: Data Mining for Cross-Sectional Data |
ch09.pdf — r12.pdf |
| 11 |
Chapter 10: Ethical Considerations in BI Hands-on Practice: - (9) Visual Analytics - Trends, Clustering, Distributions, and Forecasting — Self-study 2: Data Mining for Temporal Data |
ch10.pdf — hp09.pdf s02.pdf |
| 12 |
Mid-term Report — 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 Hands-on Practice Resources
- Danh Sách Sinh Viên (HK1/24-25)
- Dữ liệu đăng ký nhóm & Lịch báo cáo
Project Showcase 🏆
- The Project Showcase is a dedicated page to celebrate excellence in student projects from previous semeters.
- Visit the Project Showcase at here.
Staffs
-
Lecturer: Phuc H. Duong
- Email: duonghuuphuc@tdtu.edu.vn
- Website: www.duonghuuphuc.com
-
TAs:
- Nguyễn Duy Tuấn — ndtuan@fastai.dev
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.118, TDTU Campus (Tan Phong, HCMC)
- Personal email: dhp@fastai.dev
Archived
