ISM 4117 Business Intelligence

Course Information

Meeting Times:
Credit Hours:


Tsangyao Chen, Ph.D.
Office Hours:
Office Hours Come to the office hours for technical consultation or just to chat.
Online conferencing Online conferencing with the instructor when you are not able to meet in person.
Text Messaging Use text messaging, as permitted by institutional policy, for quick technical assistance.
  1. Use the institutional learning management system email instead of regular emails for threaded email correspondences.
  2. Emails are generally responded to within 24 hours.
  3. If needed, the instructor's email address is

Business intelligence (BI), by nature, is the study of an integrated set of applied computational and business techniques used to obtain business insights from data. Simply put, BI is computerized support for managerial decision-making. The purpose of using BI is to help organizations stay competitive by managing data as a strategic resource for supporting decisions and enabling innovation.
To achieve better decision-making, organizations need the capacity to collect contextual business data; to employ analytics techniques to discover possible business insights; and to communicate and collaborate the results of analysis internally and externally. This course provides a balanced introduction to BI, including both the organizational (managerial and strategic) and technical issues associated with the development and deployment of BI applications to serve as such capacity. Major topics covered include business needs, decision support, IT & decision infrastructure, data management, the analytical processes, methodologies, and current BI practices. Students will also learn commercial tools and techniques such as visualization, statistical analysis, and management dashboard to transform business data into useful information for effective decision support.

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

No textbook is required for this course. Required and suggested reading materials, if any, will be provided. The following textbooks are recommended as resources for more complete and in-depth investigation on the topics covered in class.

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.
  • Laptop Computer: You are encouraged to bring a laptop computer to the class meetings for use in the hands-on activities.
  • Software: Software applications needed for this course is available via
  • Help Desk: If you need help using the applications or have issues accessing the virtual machines, speak with the personnel at the Help Desk ( in the Department building or create a support request ticket.

Course Assignments

Submission Guidelines:
  • All assignment submissions will be accepted during the scheduled assignment submission period.
  • Late submissions will be granted only in excused situations per university attendance policy with necessary documentation.
  • Note that some assignments must be done in order. For example, in order to analyze data using certain applications, system configuration and dataset import may need to be completed in a prior assignment.


Homework assignments are designed to give students the opportunity to practice the learning from the lectures and Lab activities. Homework assignment instructions are detailed separately in each assignment.


Lab instructions are provided in the form of detailed step-by-step lab documents. The instructor will lead the lab activities by providing short lectures followed by demonstrations before students working on the exercises.
Lab exercises provide opportunities for students to:
  • learn and practice technical skills;
  • increase conceptual understanding related to skills practiced;
  • gain knowledge and skills needed for answering homework questions


The project will require you to work individually or as a group to develop a three-tier client-server application with database backend. The details on the project will be issued in a separate handout.


Exams are comprehensive assessments of student learning over a period of time. Each of the exams:
  • Will cover the materials from the lectures, lab activities, and homework assignments;
  • Will mainly not be cumulative. However, the learner will need the knowledge and skills from earlier assignments to complete the exam questions successfully.
If a makeup exam is granted, an alternative format (e.g., essay, oral, or lab assessment) may be used.


In-class short assignments and quizzes are administered during class meetings to:
  • take class attendance; and
  • assess participation and diagnose student learning.
Note: No late or makeup submissions for attendance assignments/quizzes.

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 Category Table
Course Requirement Number of Items Points per Item Total Points
Homework 10 15 150
Lab 7 10~15 100
Project 1 50 50
Exam 3 50 150
Attendance/Participation   50
Grade Scale
(the University default scale)
Letter Grade Range
A 100% to ≥ 93%
A- < 93% to ≥ 90%
B+ < 90% to ≥ 87%
B < 87% to ≥ 83%
B- < 83% to ≥ 80%
C+ < 80% to ≥ 77%
C < 77% to ≥ 73%
C- < 73% to ≥ 70%
D+ < 70% to ≥ 67%
D < 67% to ≥ 63%
D- < 63% 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
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
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 Project Report Final Submission 11:59pm
Assignment Portfolio Post #6 11:59pm