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