Business intelligence

School of Computer and Information Sciences

COURSE SYLLABUS

Course Information

ITS531 - A01 Business Intelligence

Summer 2021 First Bi-Term

Course Format: Online

CRN: 31722

 

Instructor Information

Name: Dr. Kelly Bruning

Email: [email protected]

Phone: 9897343201

Office Location: Online

Office Hours/Preferred Contact Times: Tuesdays from 8-9 pm

Course Description

This course covers theories and applications of business analytics. The focus is on

extracting business intelligence from firms' business data for various applications,

including (but not limited to) customer segmentation, customer relationship

management (CRM), personalization, online recommendation systems, web mining, and

product assortment. The emphasis is placed on the 'know-how' -- knowing how to

extract and apply business analytics to improve business decision-making.

Course Objectives

Upon completion of this course:

Understand management support system technologies

Understand foundations and technologies for decision making

Understand techniques for predictive modeling

Understand emerging trends and future impacts

Perform business reporting and visual analytics

Learner Outcomes

Compare and contrast predictive analytics with prescriptive and descriptive

analytics.

Understand the key concepts in statistical modeling, visualization, and data

mining.

Analyze the key concepts in text mining, sentiment analysis, big data, and cloud

computing,

Analyze the components of knowledge systems, the Internet of Things, and

Intelligent Applications.

Understand the main components of collaborative systems, robotics, and AI

support systems.

Course Website

Access to the course website is required via the iLearn portal on the University of the

Cumberlands website: http://www.ucumberlands.edu/ilearn/ 

or https://ucumberlands.blackboard.com/

Required Books and Resources

Title: Business Intelligence and Analytics

ISBN: 9780135192016

Authors: Ramesh Sharda, Dursun Delen, Efraim Turban

Publisher: Pearson

Publication Date: 2019-01-04

Edition: 11th ED.

Course Required text can be found and purchased via the UC Barnes and Noble

Bookstore: https://cumber.bncollege.com/shop/cumberlands/page/find-textbooks

Suggested Books and Resources

Machine Learning with Python for Everyone

ISBN: 9780134845647

Authors: Mark Fenner

Publisher: Addison-Wesley Professional

Publication Date: 2019-07-30

Analytics, Data Science, and Artificial Intelligence

ISBN: 9781292341552

Authors: RAMESH. DELEN SHARDA (DURSUN. TURBAN, EFRAIM.),

Dursun Delen, Efraim Turban

Publication Date: 2020-05-22

Requirements and Policies

Academic Dishonesty Policy

As a Christian liberal arts university committed to the pursuit of truth and

understanding, any act of academic dishonesty is especially distressing and cannot

be tolerated. In general, academic dishonesty involves the abuse and misuse of

information or people to gain an undeserved academic advantage or evaluation. 

The common forms of academic dishonesty include:

1. cheating – using deception in the taking of tests or the preparation of written

work, using unauthorized materials, copying another person’s work with or

without consent, or assisting another in such activities;

2. lying – falsifying, fabricating, or forging information in

either written or spoken presentations;

3. plagiarism –

 using the published writings, data, interpretations, or ideas of another without 

Episodes of academic dishonesty are reported as appropriate to the Vice President

for Academic Affairs. The potential penalty for academic dishonesty includes 1) a

failing grade on a particular assignment, 2) a failing grade for the entire course, 3)

suspension or expulsion, or (4) revocation of a degree.

Attendance Policy

Course enrollment and participation will be monitored and verified for all students

during the first two weeks of classes. Lack of participation during this time may

jeopardize enrollment status. Each student is expected to meet course expectations by

completing the coursework required each week. Active participation and staying abreast

of the material is essential to success. Program specific attendance policies may still

apply.

Participation Policy

Students are expected to actively participate in intelligent discussion of assigned

topics in all areas, such as: Discussion Board Activities, Synchronous Sessions,

Forums, Shared Papers, etc. 

 Point adjustments will be taken for non-participation.

Disability Accommodations

University of the Cumberlands accepts students with certified disabilities and provides

reasonable accommodations for their certified needs in the classroom, in housing, in

food service or in other areas. For accommodations to be awarded, a student must

submit a completed Accommodations Application form and provide documentation of

the disability to the Disability Services Coordinator (Mr. Jacob Ratliff, Boswell Campus

Center, Student Services Office Suite, [email protected]). When all

paperwork is on file, a meeting between the student and the Coordinator will be

arranged to discuss possible accommodations before accommodations are formally

approved. Students must then meet with the Coordinator at the beginning of each

semester before any academic accommodations can be certified for that term.

Certifications for other accommodations are normally reviewed annually.

Academic Appeal

Both undergraduate and graduate students have the right to challenge a grade. If

discussions with the course instructor and department chair do not lead to a satisfactory

conclusion, students may file a formal written appeal with the Vice President for

Academic Affairs, who will forward the appeal to the chair of the Academic Appeals

Committee. This formal written appeal must be filed by the end of the 4th week of

classes in the next regular term following the term in which the course in question was

taken. The Academic Appeals Committee then gathers information from the student, the

instructor, and any other relevant parties. The Committee will deliver its

recommendation on the complaint to the Vice President for Academic Affairs. After

reviewing this recommendation and concurring or amending it, the Vice President for

Academic Affairs will inform the student and instructor of the disposition of the

complaint no later than the last day of classes of the term in which the complaint was

filed. Records of all actions regarding academic grade appeals, including their final

disposition, are maintained by the Vice President for Academic Affairs and the Academic

Appeals Committee. (Undergraduate Catalog/Graduate Catalog)

Student Responsibilities

Students should: 

Use University of the Cumberlands email system for all academic, administrative,

and co-curricular communication between faculty, staff and peers. 

Check for email and class announcements using iLearn (primary) and University of

the Cumberlands webmail (secondary) daily. 

Demonstrate Cumberlands Character in and outside the classroom per the

University Mission & Vision

Ensure you have consistent required technology for the course

Participate in courses regularly to:

Find announcements and updates

Complete assignments on time. Keep in mind that all deadlines use Eastern

Standard Time (EST).

Engage in discussion

Connect with fellow students and faculty

Present written work in an academic and professional manner.

Take examinations on the designated dates and times. Students should make

arrangements with faculty before the designated date for any needed

accommodations. 

Contact faculty or student success coordinator with questions or concerns.

Course Policies

1. The only authorized electronic means of academic, administrative, and co-

curricular communication between University of the Cumberlands and its students

is through the UCumberlands email system (i.e. Webmail). Each student is

responsible for monitoring his/her University email account frequently. This is the

primary email account used to correspond with you directly by the University;

imperative program information is sent to this email account specifically from

campus and program office.

2. Students should check for e-mail and class announcements using iLearn (primary)

and University of the Cumberlands webmail (secondary).

3. Students are expected to find out class assignments for missed classes and make

up missed work.

4. Students are expected to find out if any changes have been made in the class or

assignment schedule.

5. Written work must be presented in a professional manner.

6. Work that is not submitted in a professional manner will not be evaluated and will

be returned as unacceptable. 

7. There is a craft to writing. Spelling, grammar, punctuation and diction (word

usage) are all tools of that craft. Writing at the collegiate level will show careful

attention to these elements of craft. 

8. Work that does not exhibit care with regard to these elements will be considered

as inadequate for college writing and graded accordingly.

9. Students are expected to take the examinations on the designated dates. If you

are unable to take the exam on the scheduled date and know in advance, you are

to make arrangements with your professor before the designated date. If you miss

the exam, you must have a legitimate reason as determined by your professor.

Recognizing that a large part of professional life is meeting deadlines, it is necessary to

develop time management and organizational skills. Failure to meet the course

deadlines may result in penalties. Keep in mind that all deadlines are set using Eastern

Standard Time (EST). Late assignments will NOT be accepted.

Course Activities and Experiences

Students are expected to:

Review any assigned reading material and prepare responses to homework

assigned.

Actively participate in activities, assignments, and discussions.

Evaluate and react to each other’s work in a supportive, constructive manner.

Complete specific assignments and exams when specified and in a professional

manner.

Utilize learned technologies for class assignments.

Connect content knowledge from core courses to practical training placement and

activities.

Links to Support

UC Academic Catalog: https://www.ucumberlands.edu/academics/academic-catalog

UC Student Handbook: https://www.ucumberlands.edu/student-handbook

Academic Resources & Writing Center: www.ucumberlands.edu/learningcommons

Library: http://www.ucumberlands.edu/library/ 

Bookstore: https://cumber.bncollege.com/shop/cumberlands/home

About University of the Cumberlands:

https://www.ucumberlands.edu/about/presidents-welcome

Instructions for Accessing, Downloading, and Activating Office 365 Pro Plus

(free for UC Students):

https://helpdesk.ucumberlands.edu/support/solutions/articles/7000045435

Course Evaluation

A student will be evaluated/weighted on the following basis:

A student will be evaluated/weighted on the following basis:

1. Exams - Each exam will consist of multiple-choice, multiple answers, matching,

and True/False questions. Exam items derived primarily from lectures and

readings. Exams will be available through iLearn and completed independently.

2. Homework Assignments, Discussion, & Quizzes – Assignments, Discussion, &

Quizzes will be given throughout the term. Each quiz will consist of multiple-

choice/answer, short answer questions, matching, and True/False questions — quiz

items derived primarily from lectures and readings. Quizzes will be available

through iLearn and completed independently. Assignments and Discussions will

come from the course lectures, materials, and required reading assignments.

3. Practical Connection Assignment – Written Assignment where students will reflect

on course concepts and their practical connection to a working environment.

4. Residency Project - Research project completed during the residency weekend.

Students will be randomly grouped in iLearn. Each group will submit their research

project as a group. Students need to bring their laptops to conduct research, write

a research paper (SafeAssign reviewed), create a PowerPoint presentation, and

present their project orally before the class. Students must attend the residency

weekend to earn a grade; there are no exceptions to this rule. Students not

attending will earn zero points and 0% as a grade.

Grading Scale

Graded work will receive a numeric score reflecting the quality of performance as given

above in evaluation methods. The overall course grade will be determined according to

the following scale:

A= 900 – 1000 (90% - 100%)

B= 800 – 899 (80% - 89%)

C = 700 – 799 (70% - 79%)

F < 699 (Below 69%)

Course Schedule

Note: Assignments in the following table are listed when they are due.

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

May 3-9 Lesson 1: Overview of Business Intelligence, Analytics, Data Science, and

Artificial Intelligence / Artificial Intelligence: Concepts, Drivers, Major Technologies,

and Business Applications

Required Readings Chapter 1 & Chapter 2 (Analytics, Data Science &

Artificial Intelligence)

0

Discussion Discussion 1 (Chapter 1): Compare and contrast

predictive analytics with prescriptive and descriptive

analytics. Use examples.

Discussion 2 (Chapter 2): Discuss the process that

generates the power of AI and discuss the

differences between machine learning and deep

learning.

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(50 points

total)

Homework Chapter 1 –discussion question #1 & exercises 5, 15

(limit to one page of analysis for question 15)

Chapter 2 – discussion question #1 & exercises 4, 5,

and 15(limit to one page of analysis for question 15)

The above assignments should be submitted in one-

word document. Include an APA cover page and a

reference page.

45 points

Attendance Quiz There is a required quiz that must be taken this week

by Wednesday. It’s a one question answer quiz. 

5 points

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

May 10-16 Lesson 2: Nature of Data, Statistical Modeling, and Visualization / Data

Mining Process, Methods, and Algorithms

Required Readings Chapter 3 & Chapter 4 (Analytics, Data Science &

Artificial Intelligence)

0

Discussion Discussion 1 (Chapter 3): Why are the original/raw

data not readily usable by analytics tasks? What are

the main data preprocessing steps? List and explain

their importance in analytics.

Discussion 2 (Chapter 4): What are the privacy

issues with data mining? Do you think they are

substantiated?

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(50 points

total)

Homework Chapter 3 –discussion question #1-4 & exercise 12 

Chapter 4 – discussion question #1-5 & Case pg.

243 The Target story answer the two case

questions on page 244 integrating concepts and

examples from that case. 

The above assignments should be submitted in one-

word document.

Include an APA cover page and a reference page.

50 points

May 17-23  Lesson 3: Machine-Learning Techniques for Predictive Analytics / Deep

Learning and Cognitive Computing

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Required Readings Chapter 5 & Chapter 6 (Analytics, Data Science &

Artificial Intelligence)

0

Discussion Discussion 1 (Chapter 5): What is the relationship

between Naïve Bayes and Bayesian networks? What

is the process of developing a Bayesian networks

model?

Discussion 2 (Chapter 6): List and briefly describe

the nine-step process in con-ducting a neural

network project.

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(50 points

total)

Homework Chapter 5 –discussion question #1-4 & exercise 6 &

internet exercise #7 

Chapter 6– discussion question #1-5 & exercise 4

The above assignments should be submitted in one-

word document.

Include an APA cover page and a reference page.

50 points

 May 24-30  Lesson 4: Text Mining, Sentiment Analysis, and Social Analytics

Required Readings Chapter 7 (Analytics, Data Science & Artificial

Intelligence)

0

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Discussion Discussion 1 (Chapter 7): What are the common

challenges with which sentiment analysis deals?

What are the most popular application areas for

sentiment analysis? Why?

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(25 points

total)

Homework Chapter 7 –discussion question #1-4 & exercise 3 &

Application Case 7.8 on pg. 447 answer the two

case questions on page 450 integrating concepts

and examples from that case. 

The above assignments should be submitted in one-

word document.

Include an APA cover page and a reference page.

50 points

Midterm MIDTERM IS DUE BY SUNDAY AT 11:59 p.m., ET 150

May 31-June 6 Lesson 5: Prescriptive Analytics: Optimization and Simulation/ Big

Data, Cloud Computing, and Location Analytics

Required Readings Chapter 8 & Chapter 9 (Analytics, Data Science &

Artificial Intelligence)

0

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Discussion Discussion 1 (Chapter 8): Excel is probably the most

popular spreadsheet software for PCs. Why? What

can we do with this package that makes it so

attractive for modeling efforts?

Discussion 2 (Chapter 9): What are the common

business problems addressed by Big Data analytics?

In the era of Big Data, are we about to witness the

end of data warehousing? Why?

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(50 points

total)

Homework Chapter 8 –discussion question #1-4 & discussion

question 10

Chapter 9- discussion question #1-5 & Page

512,-513 Answer Questions 1-5 for the Opening

Vignette "Analyzing Customer Churn in a

Telecom Company using Big Data Methods

(starts on page 510). 

The above assignments should be submitted in one-

word document.

Include an APA cover page and a reference page.

50 points

June 7-13 Lesson 6: Robotics / Group Decision Making, Collaborative Systems, and

AI Support

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Required Readings Chapter 10 & Chapter 11 (Analytics, Data Science &

Artificial Intelligence)

0

Discussion Discussion 1 (Chapter 10): There have been many

books and opinion pieces writ-ten about the impact

of AI on jobs and ideas for societal responses to

address the issues. Two ideas were mentioned in the

chapter – UBI and SIS. What are the pros and cons of

these ideas? How would these be implemented?

Discussion 2 (Chapter 11): Explain how GDSS can

increase some benefits of collaboration and decision

making in groups and eliminate or reduce some

losses.

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

each

(50 points

total)

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Homework Chapter 10 –discussion question #   1,4,5,6,7 &

exercise 5

50 points

Chapter 11- discussion question #1-4 & exercise 4

 Note Simon's Decision making Model was

previously addressed in chapter 1 on pages 9-

11. Be sure to write one page on the

comparison of Simon's Decision Model with

Group Support Systems including your own

examples (exercise 4).  

The above assignments should be submitted in one-

word document.

Include an APA cover page and a reference page.

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Portfolio Project Portfolio Project: This week discuss a current

business process in a specific industry. Note the

following:

-The current business process itself.

-The industry the business process is utilized in.

After explaining the current situation, take the

current learning from the course and:

Explain a new technology that the business

should deploy. Be specific, don’t only note the

type of technology but the specific instance of

technology. (For example, a type of technology

is smart automation a specific type of

automation is automated light-dimming

technology).

Note the pros and cons of the technology

selected.

Note various factors the business should

consider prior to deploying the new technology

The above submission should be three pages in

length. Remember the total length does not include

the APA approved cover page or the references.

There should be at least three APA approved

references to support your work. 

50 points

June 14- 20 Lesson 7: Knowledge Systems / The Internet of Things as a Platform

of Intelligent Applications

Required Readings Chapter 12 & Chapter 13 (Analytics, Data Science &

Artificial Intelligence)

0

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Discussion Discussion 1 (Chapter 12): Examine Alexa’s skill in

ordering drinks from Starbucks (only 1 discussion

this week since the Practical Connection Assignment

is also due this week)

Note: The first post should be made by Wednesday

11:59 p.m., EST. I am looking for active engagement

in the discussion. Please engage early and often.

Your response should be 250-300 words. Respond to

two postings provided by your classmates.

25 points

Homework Chapter 12 –discussion question #1-3 & exercise 1 &

12 & 16

Chapter 13- discussion question #1-4 ONLY 

The above assignments should be submitted in one-

word document

Include an APA cover page and a reference page.

Practical Connection Assignment - Submitted

as a separate Word Document in the

appropriate drop box (separate from the ch. 12

& 13 homework above)

50 points

100

points

June 21-24 (short week) Lesson 8: Implementation Issues

Grading Category Activity Title

Grade

Allocation

(% of all

graded

work)

Required Readings Chapter 14 (Analytics, Data Science & Artificial

Intelligence)

0

Discussion No Discussion THIS WEEK! 0

Homework Chapter 14–discussion question #1, #3, #4 &

exercise 2

The above assignments should be submitted in one-

word document

Include an APA cover page and a reference page.

 ALL WORK IS DUE BY WEDNESDAY THIS WEEK!

50 points

*ALL DUE DATES AND ASSIGNMENTS SUBJECT TO CHANGE

**All assignments are due in iLearn by 11:59 pm EST on SUNDAY of the corresponding

week…EXCEPT the last week of class when all assignments are due on WEDNESDAY at

11:59 pm EST.

Syllabus Disclaimer

This syllabus contains important information critical to your success in this course. It

includes guidelines for this course and the instructor’s current expectations about

content, schedule, and requirements necessary for each student to achieve the best

educational results. While you must review and become familiar with the contents of

this syllabus, the instructor reserves the right to make adjustments or change in the

syllabus from time to time. Any changes to the syllabus will be discussed with the

students.