CS504049 - Business Intelligence Systems

Fall, 2021

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.


  1. 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. [PDF]
  2. Joshua N. Milligan. Learning Tableau 2020, Fourth Edition. Packt Publishing, 2020.
  3. Tom Mitchell. Machine Learning. McGraw Hill, 1997. [PDF]
  4. Carlo Vercellis. Business Intelligence: Data Mining and Optimization for Decision Making. New York: Wiley, 2009. [PDF]


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


Group Day Google Classroom Google Meet
N2 Friday [1] |


Weeks Topics Resources
1 Course Introduction
Machine Learning for Predictive Data Analytics
2 Decision Tree Learning - Part 1: Classification Week-02.pdf
3 Hands-on Practice:
- (1) Taking Off with Tableau
- (2) Connecting to Data in Tableau
4 Decision Tree Learning - Part 2: Regression Week-04.pdf
5 Hands-on Practice:
- (3) Moving Beyond Basic Visualizations
- (4) Starting an Adventure with Calculations and Parameters
6 Statistics Fundamentals
Hands-on Practice:
- (5) Leveraging Level of Detail Calculations
- (6) Diving Deep with Table Calculations
7 Hands-on Practice:
- (7) Making Visualizations That Look Great and Work Well
- (8) Telling a Data Story with Dashboards
8 Hands-on Practice:
- (9) Visual Analytics - Trends, Clustering, Distributions, and Forecasting
9 Bayesian Learning Week-09.pdf
10 Association Analysis: Basic Concepts Week-10A.pdf
11 Mid-term Examination N/A
12 Final Project Presentation
Hands-on Practice:
- (10) Advanced Visualizations
- (11) Dynamic Dashboards
13 Final Project Presentation
Hands-on Practice:
- (12) Understanding the Tableau Data Model, Joins, and Blends
14 Final Project Presentation
Hands-on Practice:
- (13) Structuring Messy Data to Work Well in Tableau
- (14) Taming Data with Tableau Prep
15 Final Project Presentation
Hands-on Practice (Self-study):
- (15) Sharing Your Data Story


Final Project

  • Sample Proposal
  • Useful links


  • Kế hoạch dạy-học từ Tuần 08
  • Danh sách nhóm & phân công báo cáo
  • We host our learning forum on this Facebook Group
  • Attendance Forms:
    • Lecture session
    • Check-in data
    • Check-in code will be informed when giving the corresponding lesson.
    • Student email account provided by TDTU is required to access the attendance forms and other resources.


  • Lecturer: Phuc H. Duong, M.Sc.
  • Teaching assistants:
    • Che Hoang Huy (Student) - chhuy [at] newai.com.vn
    • Phan The An (Student) - ptan [at] newai.com.vn


  • 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.


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