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ISM 4117 Business Intelligence (BI)

Course Information

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Prerequisites:

Instructor

Tsangyao Chen, Ph.D.
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Email: ty@tychen.org
Business Intelligence (BI) is the interdisciplinary study of technologies, processes, and strategies that transform data into actionable insights to support managerial decision-making. This course offers a comprehensive introduction to BI, emphasizing both the organizational and technical dimensions of designing and implementing effective BI solutions. Students will explore how organizations can leverage data as a strategic asset to enhance competitiveness, support innovation, and improve decision quality. Topics covered include business needs analysis, decision support systems, IT and decision infrastructure, data management, analytical methodologies, and current BI practices. The course also covers practical skills in using commercial BI tools for data visualization, statistical analysis, and dashboard development. Through case studies and hands-on exercises, students will gain experience in collecting and analyzing contextual business data, applying analytical techniques, and communicating insights to support collaborative, data-informed decision-making.
Business Intelligence (BI), Decision Support, Dashboard, Excel, SQL, Data Visualization, Data Analysis

Learning Objectives

After the successful completion of this course, students will be able to:

  1. Demonstrate an understanding of BI concepts both as an academic research domain and an industry field.
  2. Manage and integrate data and information to enable BI analysis, reporting, visualization, and analytics tasks.
  3. Use BI tools, technology, and techniques to perform BI operations in support of organizational decision-making.
  4. Design, develop, implement, and analyze BI applications.
  5. Evaluate the effectiveness of BI activities in supporting organizational decision-making and performance.

Course Materials

Technical Resources

  • Benton, C. J. (n.d.). Excel 2019 pivot tables & introduction to dashboards: The step-by-step guide (3rd ed.).
  • Meier, M., & Baldwin, D. (2021). Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau (3rd ed.). Packt Publishing Ltd.

General Introduction

  • Sharda, R., Delen, D., & Turban, E. (2017). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th edition). Pearson.
  • Skyrius, R. (2021). Business intelligence: A comprehensive approach to information needs, technologies and culture. Springer.
  • Howson, C. (2013). Successful business intelligence: Unlock the value of BI & big data (2nd edition). McGraw Hill.

Course Assignments

Grading Scheme

Tis course intends to enable students to complete all of these activities following the “learning by doing” principle. The grading scale is based on the assumption that the students will work independently and collaboratively to complete all the activities with very few errors. Generally, a student attending all the class meetings and complete all the assignments by schedule will do very well in this course, even with minimal prior technical experience.
Grade Categories
Course Requirement Number of Items Points per Item Total Points
Homework 10 10 100
Lab 10 10 100
Project 1 50 50
Exam 2 75 150
Attendance/Participation   100
      500

The final grade will be calculated based on the total points earned by the student. The final grade will be determined by the following scale:
Grade Scale
Letter Grade Range
A 100% to ≥ 90%
B < 90% to ≥ 80%
C < 80% to ≥ 70%
D < 70% to ≥ 60%
F < 60% to ≥ 0%

Course Schedule

Week Module Topic Lab Reading Assignment
1 BI Overview
  • Course Introduction: BI Overview 1
  • BI job market
  • BI definitions
  • BI system components & BI as IS
2 BI Overview
  • BI scopes: BI vs BA
  • BI enterprise product (SAP & Oracle)
  • Business functions & enterprise systems
  • Excel for BI Lab I (PivotTables)
  • VM
  • Linux user mgmt File & directories
3 Nature of Data: Insights from Excel Business Reporting
  • Decision-results cycle
  • BI products: Enterprise vs Dept BI; strategic vs operational; reporting vs. predictive
  • Excel for BI Lab II (PivotTables)
  • Dummies 18
  • WordPress
  • Dummies 41, 43
  • Excel PivotTable (crime, fruit)
MS Excel BI Excel
4 Nature of Data: Insights from Excel Business Reporting
  • BI process model (Sky p.34)
  • KPI, metrics
  • Excel Dashboard
Excel
5 Querying & Datawarehouse (DW)
  • BI and ES (sky p.36); Domains (sky 45); BI dimensions (sky 55)
  • CRUD
  • DW
  • Fact Table & Star Schema
  • Data processing
  • SQL server on Linux
  • MySQLBench
  • DW: Dummies 26
  • WWI sqls
SDT: 66 SDT03
6 Querying & Datawarehouse (DW)
  • OLAP
  • Data Warehousing with SQL Server
  • Dummies 70
  • SQL server in docker
  • AdventureWorksDW
  • Azure DW samples
7 Data Analytics with Python
  • The Data science process
  • Pipeline
  • PyCharm
  • Jupiter Notebook
  • VS Code
8 Data Analytics with Python
  • Python review 1
  • External data sources (CI)
  • Web Data Scrapping
  • Open Datasets
 Midterm
9 Data Analytics with Python
  • Python Review 2
  • Data processing
  • Jupiter Notebook
10 Data Analytics with Python
  • BI Maturity Model
  • BI Culture
  • Stored Procedure
11 Visualization & Analytics using Tableau
  • Graph/charts
  • Report
  • Dashboard
 Larsen & Chang Project plan
12 Visualization & Analytics using Tableau
  • Integrating Python
13 BI Project Development
  • BI Architecture
 Dummies 54
14 BI Project Development
  • Standard queries for Dashboard
  • Integrating SQL
15 BI Project Development
  • Final Exam
 Final Exam
16 Project Presentation Final Project PPT
All course assignments and texts with due dates are listed below. To be successful in this course, be sure to complete and submit all required assignments by the due date.
Week Date Assignment Due
Assignment VM List 11:59pm
Assignment SCP test.txt to the VM and local class directory 11:59pm
Assignment PivotTable: CrimeData & FruitSales 11:59pm
Assignment VM Account Info 11:59pm
Assignment Install WordPress on Your Own 11:59pm
Assignment Syllabus and Course Policies 11:59pm
Assignment Excel BI 01 11:59pm
Assignment Excel BI 02 11:59pm
Assignment Excel BI 03: Dashboard 11:59pm
Assignment CRUD MySQL employees DB (02-14) 11:59pm
Assignment SQL Script CRUD Lab 11:59pm
Assignment Your Portfolio Site & First Post 11:59pm
Assignment Midterm Exam 12:50pm
Assignment Portfolio 2nd Post 11:59pm
Assignment GROUP BY Lab (3col12q) 11:59pm
Assignment Portfolio 3rd Post 11:59pm
Assignment Business Questions Set #1 11:59pm
Assignment Portfolio 4th Post 11:59pm
Assignment Project Proposal 11:59pm
Assignment Business Questions Set #2 11:59pm
Assignment I did Excel Pivot+SQL 11:59pm
Assignment hello_world 11:59pm
Assignment Python Lab #2 11:59pm
Assignment Portfolio Post #5 11:59pm
Assignment python2CSV 11:59pm
Assignment Python Lab #1 11:59pm
Assignment Portfolio Post 6 11:59pm
Assignment Project Progress Report 11:59pm
Assignment Project Progress Report #2 11:59pm
Assignment Portfolio Post #5 11:59pm
Assignment Project Presentation/Review 11:59pm
Quiz Final Exam 11:59pm
Assignment Project Report Final Submission 11:59pm
Assignment Portfolio Post #6 11:59pm
Assignment Extra Credit