UNIVERSITI TUNKU ABDUL RAHMAN (UTAR)
FACULTY OF ACCOUNTANCY AND MANAGEMENT
COURSE PLAN
1.
2.
Course Code &
Course Name:
Program of
Study:
3.
Year of Study:
4.
Year &
Trimester:
Credit Hour
Credit Hour &
Contact Hour:
Name(s) of
Academic
Staff:
Moderator
5.
6
7.
8.
9.
Mode of
Delivery:
10. Course
Objective:
UKEQ3133/UKEQ3193 Business Analytics
Bachelor of Accounting (Honours)
Bachelor of Economics (Honours) Global Economics
Bachelor of Finance (Financial Technology) with Honours
Year Three
202401
3 credit hours
2 hours lecture per week for the duration of 14 weeks
1 hour tutorial per week for the duration of 14 weeks
Lead Lecturer: Yow Taw Onn, yowto@utar.edu.my
Tutor(s): Yow Taw Onn, yowto@utar.edu.my
Dr. Ooi Tze Wei, ooitw@utar.edu.my
Lecture and Tutorial
This course explores the descriptive, predictive and descriptive parts of business analytics.
The important concepts of effective data visualization techniques are introduced and
discussed. Students learn various analytical methods and models used in data and business
analysis. The focus is on understanding the statistical figures reported and successfully
interpret these statistical figures into effective business meanings that aid in business
decisions making.
Emphasis is placed on applications, concepts and interpretation of results. Students use a
computer software package for data analysis.
11. Course
Learning
Outcome:
After completing this unit, students should be able to:
CLO1. Apply analytical thinking and make data-driven decisions to optimize the
business process.
CLO2. Apply data visualization techniques to effectively communicate data, information
and insights.
CLO3. Analyze the concepts and practices in descriptive, predictive and prescriptive
analytics.
CLO4. Evaluate the concepts of analytical methods and models available for use in
business analytics (including supervised and unsupervised learning methods) and their
relevance.
CLO5. Create presentations/infographics/reports with relevant information from data
analysis, to convincingly and effectively impact business decisions.
Course Plan of UKEQ3133/UKEQ3193 Business Analytics
12. References:
Main References:
1. Camm. Cochran. Fry. Ohlmann. Anderson. Sweeney. Williams. (2017). Essentials of
Business Analytics (2nd ed). Cengage Learning.
Additional Reference(s):
1. Bhimasankaram Pochiraju, Sridhar Seshadri (2019). Essentials of Business
Analytics: An Introduction to the Methodology and its Applications.
Springer.
2. Provost, Foster. Fawcett, Tom. (2013). Data Science for Business (1st ed).
O’Reilly Media Inc.
3. Abbot, Dean. (2014). Applied Predictive Analytics – Principles and
Techniques for the Professional Data Analyst (1st ed). John Wiley and Sons,
Inc.
CLO
PLO
Delivery
Methods
C/A/P and
Taxonomy
Level
Assessment
Methods & Mark
Breakdown*
Total
Final Exam
No
Assignment
Constructive
Alignment
Table
Test
13
1
CLO1
1
Lecture,
Tutorial
C3
10%
10%
2
CLO2
2
Lecture,
Tutorial
C6
15%
15%
3
CLO3
2
Lecture,
Tutorial
C4
4
CLO4
6
Lecture,
Tutorial
C5
5
CLO5
7
Lecture,
Tutorial
C6
Total
Course Plan of UKEQ3133/UKEQ3193 Business Analytics
20%
20%
40%
15%
20%
40%
40%
15%
40%
100%
14. Assessment
Methods:
No.
1.
Method of Assessment
Continuous Assessment
Mid-Term Test
Group Assignment
Total
Final Examination
2.
GRAND TOTAL
Total
60%
20%
40%
60%
(40 marks)
(80 marks)
(120 marks)
(100 marks)
40%
100%
1. Continuous Assessment (100%)
a) Mid-term test
20%
(20 marks)
Tests will be given to monitor students’ progress on the understanding of the lectures. The
duration of the test will be ONE (1) hour and will be scheduled at WEEK 7. The testwill
cover Descriptive and Predictive Analytics.
b) Group Assignment
40%
(50 marks)
Students are required to complete two tasks for group assignment where a
presentation/infographic needs to be created. Students need to form a group of a maximum
5students from the same tutorial group and the assignment will be distributed on/before
WEEK 2. A penalty of 10% reduction of the maximum mark applicable to the assessment
will be levied for each day of late submission.
2. Final Examination (40%)
The final examination for this course will be 2.5 hours and will consist of TWO sections:
Section A (40 marks) = ONE (1) compulsory question.
Section B (60 marks) = THREE (3) questions in which students are required to answer
any TWO (2) questions.
15. Other
Additional
Information:
16. Remark:
Students who are taking the UKEQ3193 must comply with the compulsory passing
requirement. Please be informed that starting from the May 2022 intake onwards, students
are required to score a minimum of 40% in the Final Examination and obtain an overall
mark of 50% and above to pass the specialization courses.This rule also implies that students
would be graded as “Fail” (F grade) if he or she scored 39% or below in the Final
Examination regardless of the total score obtainedfor the course.
Remarks: The requirement of a compulsory passing rate for the final examination does
NOT apply to ALL other programs (not listed above) students.
ACADEMIC REGULATIONS
Plagiarism
Plagiarism is defined as the submission or presentation of work, in any form, which is not
one’s own, without acknowledgment of the sources. If a student obtains information or
ideas from an outside source, that source must be acknowledged. Another rule to follow is
that any direct quotation must be placed in quotation marks and the source immediately
cited.
Plagiarism is also defined as copy of all or part of the work of another student(s) of current
or previous batch of this University or another higher learning institution.
Course Plan of UKEQ3133/UKEQ3193 Business Analytics
The University’s degree and other academic awards are given in recognition of the
candidate’s personal achievement. Plagiarism is therefore considered an act of academic
fraudulence and an offence against University discipline.
Intellectual Property
Copyright must be seriously protected. The University takes a strong stand against any
illegal photocopying of textbooks and any other materials by students. Students are
forewarned of the consequences and the penalty that may be meted out if they are “caught
in the act”.
Mode of Referencing
Students are advised to incorporate proper academic modes of referencing. The normally
acceptable mode of academic referencing is the American Psychological Association
(APA) system; please refer to the attached APA referencing system document for detailed
usage (See Appendix 1).
Teaching Plan
Week
1
(29/01/24 02/02/24)
2
(05/02/24 09/02/24)
Topic
Introduction to Business Analytics –
Part 1

Decision making in business

Business analytics defined
Introduction to Business Analytics –
Part 2


3
(12/02/24 16/02/24)
4
(19/02/24 23/02/24)
Big Data and Business
Analytics
Statistics for Business Analytics
Data Visualization


Visualization principles
Use of tables and charts

Advanced data visualization -

Data dashboards, etc
Storytelling using data
Descriptive/Predictive Analytics – Part
1

Tutorial/Assignment
References
Camm,
Chapter 1
Provost,
Chapter 13
Tutorial 1:
Introduction – Business
Analytics in Practice
Camm,
Chapter 1
Tutorial 2:
Statistics for Business
Analytics
Camm,
Chapter 3
Tutorial 3:
Data Visualization
Camm,
Chapter 7
Linear regression
Course Plan of UKEQ3133/UKEQ3193 Business Analytics
5
(26/02/24 –
01/03/24)
Descriptive/Predictive Analytics – Part
6
(04/03/24 08/03/24)
Descriptive/Predictive Analytics – Part
3
2




7
(11/03/24 15/03/24)
Non-linear regression
Data Mining – Supervised
learning and Unsupervised
learning
Spreadsheet models – building
and auditing models, what-if
analysis.

Data Preparation and
Preprocessing

Unsupervised Learning
8
(18/03/24 22/03/24)
Predictive Analytics – Part 2
9
(25/03/24 29/03/24)
Predictive Analytics – Part 3
10
(01/04/24 05/04/24)
Prescriptive Analytics – Part 1
11
(08/04/24 12/04/24)


Unsupervised Learning (cont’)
Supervised Learning
Over-fitting and its avoidance

Working with Spreadsheet
models


Linear optimization models
Sensitivity Analysis
Prescriptive Analytics – Part 2

Camm,
Chapter 7
Analytics – Part 1
Tutorial 5:
Descriptive/Predictive
Camm,
Chapter 4,8
Analytics – Part 2
Assignment
Task 1
Submission
08/03/2024
Time series analysis and
forecasting
Predictive Analytics – Part 1

Tutorial 4:
Descriptive/Predictive
Integer Linear optimization
models
Tutorial 6:
Descriptive/Predictive
Analytics – Part 3
Camm,
Chapter 9
Mid-Term Test
Tutorial 7:
Predictive Analytics – Part 1
Camm,
Chapter 9
Predictive Analytics – Part 2
Camm,
Chapter 9
Tutorial 9:
Predictive Analytics – Part 3
Camm,
Chapter 10,11
Tutorial 8:
Tutorial 10:
Prescriptive Analytics – Part
1
Course Plan of UKEQ3133/UKEQ3193 Business Analytics
Camm,
Chapter 12
Assignment Task 2
Submission
08/04/2024
12
(15/04/24 19/04/24)
Applications – Part 1
13
(22/04/24 26/04/24)
Applications – Part 2
14
(29/04/24 03/05/24)

Retail analytics

Marketing analytics

Financial analytics

Social media and web analytics
Revision
Tutorial 11:
Prescriptive Analytics – Part
2
Tutorial 12:
Applications – Part 1
Bhimasankaram,
Chapter 18,19
Bhimasankaram,
Chapter 20,21
Tutorial 13:
Applications – Part 2
Notes: The information provided in this Course Plan is subject to change by the Lecturers. Students shall be notified
in advance of any changes.
Course Plan of UKEQ3133/UKEQ3193 Business Analytics