Relate course work to the current job responsibilities
Lakshmi143
-Business-Intelligencecoursework.pdf
School of Computer and Information Sciences
COURSE SYLLABUS
Course Information
ITS531 - A02 Business Intelligence
Fall 2020 First Bi-Term
Course Format: Online
CRN: 11674
Instructor Information
Name: Jamia Mills
Email: [email protected]
Phone: Contact via Email
Office Location: Remote
Office Hours/Preferred Contact Times: By Appointment
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:
Perform business reporting and visual analytics
Understand management support system technologies
Understand foundations and technologies for decision making
Understand techniques for predictive modeling
Understand emerging trends and future impacts
Learner Outcomes
Understand the main components of collaborative systems, robotics, and AI
support systems.
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.
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 Integrity/Plagiarism
At 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:
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.
Lying – falsifying, fabricating, or forging information in either written, spoken, or
video presentations.
Plagiarism—using the published writings, data, interpretations, or ideas of another
without proper documentation
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Lesson 1: (8/24/20 - 8/30/20) Overview of Business Intelligence, Analytics, Data
Science, and Artificial Intelligence
Required Readings Chapter 1 (Analytics, Data Science & Artificial
Intelligence)
Discussion Discussion 1: Compare and contrast predictive
analytics with prescriptive and descriptive analytics.
Use examples.
Discussion 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.
Homework Discussion question #1
Discussion question #2
Start of the Semester Quiz (must be taken by
Wednesday for participation)
Chapter 01 Exam
15 points
15 points
10 points
25 points
Lesson 2: (8/31/20 - 9/6/20) Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications/ Nature of Data, Statistical Modeling, and
Visualization
Required Readings Chapter 2 & Chapter 3 (Analytics, Data Science &
Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1: 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 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.
Homework Discussion question #1
Discussion question #2
Chapter 02 Exam
Chapter 03 Exam
15 points
15 points
25 points
25 points
Lesson 3: (9/7/20 - 9/13/20) Data Mining Process, Methods, and Algorithms/
Machine-Learning Techniques for Predictive Analytics
Required Readings Chapter 4 & Chapter 5 (Analytics, Data Science &
Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1: What is the relationship between
Naïve Bayes and Bayesian networks? What is the
process of developing a Bayesian networks model?
Discussion 2: 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.
Homework Discussion question #1
Discussion question #2
Lab # 1
Chapter 04 Exam
Chapter 05 Exam
15 points
15 points
50 points
25 points
25 points
Lesson 4: (9/14/20 - 9/20/20) Deep Learning and Cognitive Computing/Text
Mining, Sentiment Analysis, and Social Analytics
Required Readings Chapter 6 and Chapter 7 (Analytics, Data Science &
Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1 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.
Homework Discussion question #1
Lab #2
Chapter 06 Exam
Chapter 07 Exam
15 points
50 points
25 points
25 points
Lesson 5: (9/21/20 - 0/27/20) Prescriptive Analytics: Optimization and Simulation/
Big Data, Cloud Computing, and Location Analytics
Required Readings Chapter 8 & Chapter 9 (Analytics, Data Science &
Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1: 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: 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.
Homework Discussion question #1
Discussion question #2
Practical Connections Activity
Chapter 08 Exam
Chapter 09 Exam
15 points
15 points
70 points
25 points
25 points
Lesson 6: (9/28/20 - 10/4/20) Robotics / Group Decision Making, Collaborative
Systems, and AI Support/Knowledge Systems
Required Readings Chapter 10, Chapter 11 & Chapter 12 (Analytics,
Data Science & Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1: 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: 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.
Homework Discussion question #1
Discussion question #2
Chapter 10 Exam
Chapter 11 Exam
Chapter 12 Exam
Lab #3
15 points
15 points
25 points
25 points
25 points
50 points
Lesson 7: (10/5/20 - 10/11/20) The Internet of Things as a Platform of Intelligent
Applications/Implementation Issues
Required Readings Chapter 13 & Chapter 14 (Analytics, Data Science &
Artificial Intelligence)
Grading Category Activity Title
Grade
Allocation
(% of all
graded
work)
Discussion Discussion 1: Examine Alexa’s skill in ordering
drinks from Starbucks
Discussion 2: Research Apple Home Pod. How does
it interact with smart home devices? Alexa is now
connected to smart home devices such as
thermostats and microwaves. Find examples of
other appliances that are connected to Alexa and
write a report.
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.
Homework Discussion question #1
Discussion question #2
Chapter 13 Exam
Chapter 14 Exam
Lab #4
15 points
15 points
25 points
25 points
50 points
Lesson 8: (10/12/20 - 10/16/20) Course Wrapup
Required Readings No Required Readings this week
Discussion No Discussion THIS WEEK!
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
Describe the use of visualizations and why
they are important to use to tell a story about
data
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.
Homework Portfolio Project
ALL WORK IS DUE BY WEDNESDAY THIS WEEK!
175 points
*ALL DUE DATES AND ASSIGNMENTS SUBJECT TO CHANGE
