MIST 425/525 Business Intelligence

Course Information

Class:
Classroom:
Credit Hours:
Prerequisites:

Instructor

Tsangyao Chen, Ph.D.
Office:
Office Hours:
Email: ty@tychen.org

Course Overview

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.

Equipment

  • 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 https://its.university.edu.

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

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

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

Project

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.

Examination

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.

Attendance/Participation

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 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