Course Information
Meeting Times:
Classroom:
Credit Hours:
Prerequisites:
Instructor
Tsangyao Chen, Ph.D.
Office:
Office Hours:
Email:
Messaging:
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.
Emailing
- Use the institutional learning management system email instead of regular emails for threaded email correspondences.
- Emails are generally responded to within 24 hours.
- If needed, the instructor's email address is tychen@university.edu.
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:
- Demonstrate an understanding of BI concepts both as an academic research domain and an industry field.
- Manage and integrate data and information to enable BI analysis, reporting, visualization, and analytics tasks.
- Use BI tools, technology, and techniques to perform BI operations in support of organizational decision-making.
- Design, develop, implement, and analyze BI applications.
- 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.
Note:
- 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://labs.university.edu.
- Help Desk: If you need help using the applications or have issues accessing the virtual machines, speak with the personnel at the Help Desk (https://helpdesk.university.edu) 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
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.
| 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 | ||
| 500 |
| 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 |
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| 2 | BI Overview |
|
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| 3 | Nature of Data: Insights from Excel Business Reporting |
|
|
MS Excel BI | Excel |
| 4 | Nature of Data: Insights from Excel Business Reporting |
|
|
Excel | |
| 5 | Querying & Datawarehouse (DW) |
|
|
SDT: 66 SDT03 | |
| 6 | Querying & Datawarehouse (DW) |
|
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||
| 7 | Data Analytics with Python |
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| 8 | Data Analytics with Python |
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|
Midterm | |
| 9 | Data Analytics with Python |
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| 10 | Data Analytics with Python |
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| 11 | Visualization & Analytics using Tableau |
|
|
Larsen & Chang | Project plan |
| 12 | Visualization & Analytics using Tableau |
|
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| 13 | BI Project Development |
|
Dummies 54 | ||
| 14 | BI Project Development |
|
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| 15 | BI Project Development |
|
Final Exam | ||
| 16 | Project Presentation | Final Project PPT |
Assignment Schedule
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 |