CS504049 - Business Intelligence Systems

Fall, 2024

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

  1. Ramesh Sharda, Dursun Delen, Efraim Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th edition. Pearson, 2022.
  2. Joshua N. Milligan. Learning Tableau 2020, 4th Edition. Packt Publishing, 2020.
  3. 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
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

Staffs

  • Lecturer: Phuc H. Duong
  • 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

  • Recorded lecture videos.
  • Fall, 2024 (Current) | Fall, 2023 | Fall, 2021
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