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

1. Wilfried Grossmann, Stefanie Rinderle-Ma. Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg, 2015.
2. 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.
3. Joshua N. Milligan. Learning Tableau 2020, Fourth Edition. Packt Publishing, 2020.
4. Tom Mitchell. Machine Learning. McGraw Hill, 1997.
5. Carlo Vercellis. Business Intelligence: Data Mining and Optimization for Decision Making. New York: Wiley, 2009.

Evaluation

Evaluation categories	Weight (%)	Types
Process evaluation 1	10	Process Exercise
Process evaluation 2	20	Essay
Mid-term examination	20	Presentation
Final examination	50	Report

Schedule (Tentative)

Group	Day	Google Classroom	Room
N1 (TC)	Monday [2]	|	TBA
N2 (CLC)	Wednesday [2]	|	TBA

Syllabus

Weeks	Topics	Resources
1	Chapter 0: Course Introduction Chapter 1: Definition of Business Intelligence	ch00.pdf ch01.pdf
2	Chapter 2: Modeling in Business Intelligence — Hands-on Practice: - (1) Taking Off with Tableau - (2) Connecting to Data in Tableau	ch02.pdf hp01.pdf hp02.pdf
3	Hands-on Practice: - (3) Moving Beyond Basic Visualizations - (4) Starting an Adventure with Calculations and Parameters	hp03.pdf hp04.pdf
4	Chapter 3: Data Provisioning	ch03.pdf
5	Hands-on Practice: - (5) Leveraging Level of Detail Calculations - (6) Diving Deep with Table Calculations	hp05.pdf hp06.pdf
6	Chapter 4: Decision Tree Learning - Part 1: Classification	ch04.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	ch05.pdf
9	Chapter 6: Bayesian Learning	ch06.pdf
10	Chapter 7: Data Mining for Cross-Sectional Data	ch07.pdf
11	Hands-on Practice: - (9) Visual Analytics - Trends, Clustering, Distributions, and Forecasting	hp09.pdf
12	Hands-on Practice: - (10) Advanced Visualizations - (11) Dynamic Dashboards	hp10.pdf hp11.pdf
13	Mid-term Report	—
14	Final Project Report	—
15	Final Project Report	—

Resources

• Tableau Desktop [Version 2023.1.0 (20231.23.0310.1044) 64-bit] [macOS | Windows 64-bit]
• Tableau Hands-on Practice Resources

Staffs

• Lecturer: Phuc H. Duong

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

• Course homepage [NEW]: https://cs504049.fastai.dev
• Course homepage [OLD]: https://cs504049.duonghuuphuc.com
• Email: duonghuuphuc [at] tdtu.edu.vn
• Email: dhp [at] fastai.dev

Archived

• Recorded lecture videos.
• Fall, 2021 (Current)