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COMM3501 Quantitative Business Analytics
Course Details & Outcomes
Course Description
Quantitative business analytics empower business decision makers to analyse complex business problems, and make better and faster decisions. It is an essential skill for an analyst in all business disciplines to use modern analytical tools and quantitative techniques. This course aims to provide students with quantitative techniques used in business analytics, with a particular focus on practical applications of modelling skills and analytical tools (e.g. using R). The course focuses on how to select appropriate predictive modelling techniques for a stated situation and how to evaluate the suitability of a model, taking into account business context and objectives. The course covers topics including regression techniques and classification methods, model selection and validation methods, linear and non-linear models, decision trees, supervised and unsupervised learning techniques, and ethical, social and regulatory issues associated with quantitative analytics. A particular focus will be placed on communication of technical results to a wide range of business decision making audiences.
Relationship to Other Courses
The aims of this course are to provide students with an understanding of the main techniques of predictive analytics and data analytics techniques of particular relevance to business analytics, including
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Regression techniques and classification methods
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Model selection and validation methods including cross-validation and dimension reduction
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Linear and Non-linear models
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Decision Trees and extensions
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Supervised and unsupervised learning techniques
Course Learning Outcomes
| Course Learning Outcomes | Program learning outcomes |
|---|---|
| CLO1 : Explain the theories and practices of quantitative data analytics in the context of business applications |
|
| CLO2 : Critically evaluate quantitative analytics models |
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| CLO3 : Apply quantitative analytics tools and techniques to a range of business problems |
|
| CLO4 : Identify and explain the ethical, social and regulatory issues associated with the use of data and analytics techniques |
|
| CLO5 : Communicate business analytics results and insights effectively to a variety of audiences |
|
| Course Learning Outcomes | Assessment Item |
|---|---|
| CLO1 : Explain the theories and practices of quantitative data analytics in the context of business applications |
|
| CLO2 : Critically evaluate quantitative analytics models |
|
| CLO3 : Apply quantitative analytics tools and techniques to a range of business problems |
|
| CLO4 : Identify and explain the ethical, social and regulatory issues associated with the use of data and analytics techniques |
|
| CLO5 : Communicate business analytics results and insights effectively to a variety of audiences |
|
Learning and Teaching Technologies
Moodle - Learning Management System
Learning and Teaching in this course
The approach adopted in this course is a “blended” classroom. This approach integrates student-centred, in-class (live) learning with self-study (home) learning. In this “blended” approach, the first conceptual encounter with the materials happens at home when students study the relevant course material (e.g. video lectures, lecture notes and reading lists). The second conceptual encounter with the material of a given module happens in class (live online) to deepen the understanding of related topics, spark students’ interest with practical case studies, answer students' questions in the self-study process and provide a context for the subsequent modules and lab sessions. In a lecture, the lecturer provides a high-level summary of the key concepts of the module and runs other activities (such as discussions, advanced exercises, guest lectures, real-life applications) that aim to cement students’ learning. Finally, the students move on to practicing their knowledge via in-class (live online) tutorials in small groups. Tutorial sessions aim to equip students with the application and implementation skills using software (R, R Studio, R Markdown) by solving real-world problems and provide personalised help on a weekly basis. This course consists of:
Self-study course material available on the course Moodle website (e.g. textbook chapters, video lectures, lecture notes, exercises/questions),
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Weekly lectures,
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Weekly tutorials, and
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Weekly consultation times.
Additional Course Information
In order to pass this course students must:
- Achieve a composite mark of at least 50 out of 100
- Engage actively in course learning activities and attempt all assessment requirements
- Meet any additional requirements specified in the assessment details
- Meet the specified attendance requirements of the course
Assessments
Assessment Structure
| Assessment Item | Weight | Relevant Dates | Program learning outcomes |
|---|---|---|---|
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Reflection Journal
Assessment FormatIndividual
Short ExtensionYes (7 days)
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10% |
Due DatePart A Week 3, Part B Week 10
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|
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Weekly Discussion
Assessment FormatIndividual
Short ExtensionYes (7 days)
|
20% |
|
|
|
Group Project
Assessment FormatGroup
Short ExtensionYes (7 days)
|
30% |
Due DateWeek 8: 15 July - 21 July
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|
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Individual Assignment
Assessment FormatIndividual
Short ExtensionYes (7 days)
|
40% |
Start DateNot Applicable
Due DateWeek 11: 05 August - 11 August
|
|
Assessment Details
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Reflection Journal
Assessment Overview
In this assessment, you will be expected to complete a two-part task focused on critical reflection. In Part A, you will reflect on your previous learning experiences and outline your goals for personal development within the subject of Quantitative Business Analytics. In Part B, you will evaluate your learning experience throughout the course, emphasising your contributions and others to the group task, collaboration with peers, and the influence you had on others. This task will require you to suggest ways in which future team collaborations might be improved and discuss options for continuous development for the individual.
Reflection Journal assesses: PLO2, PLO3, PLO7
BCom students: myBCom course points for PLO7
Course Learning Outcomes
- CLO1 : Explain the theories and practices of quantitative data analytics in the context of business applications
- CLO2 : Critically evaluate quantitative analytics models
- CLO3 : Apply quantitative analytics tools and techniques to a range of business problems
- CLO4 : Identify and explain the ethical, social and regulatory issues associated with the use of data and analytics techniques
- CLO5 : Communicate business analytics results and insights effectively to a variety of audiences
Detailed Assessment Description
In this assessment, you will be expected to complete a two-part task focused on critical reflection. In Part A, you will reflect on your previous learning experiences and outline your goals for personal development within the subject of Quantitative Business Analytics. In Part B, you will evaluate your learning experience throughout the course, emphasising your contributions and others to the group task, collaboration with peers, and the influence you had on others. This task will require you to suggest ways in which future team collaborations might be improved and discuss options for continuous development for the individual.
Reflection Journal assesses: PLO2, PLO3, PLO7
BCom students: myBCom course points for PLO7
Assignment submission Turnitin type
This assignment is submitted through Turnitin and students can see Turnitin similarity reports.
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Weekly Discussion
Assessment Overview
This course includes weekly formative activities, such as online discussion questions and class discussions, designed to reinforce the concepts learned each week. These activities encourage students to stay engaged with the course materials, helping them identify areas for improvement and enhancing their overall learning experience.
Weekly Discussion assess: PLO1, PLO2, PLO3
BCom students: myBCom course points for PLO2
Course Learning Outcomes
- CLO1 : Explain the theories and practices of quantitative data analytics in the context of business applications
- CLO2 : Critically evaluate quantitative analytics models
- CLO3 : Apply quantitative analytics tools and techniques to a range of business problems
- CLO4 : Identify and explain the ethical, social and regulatory issues associated with the use of data and analytics techniques
- CLO5 : Communicate business analytics results and insights effectively to a variety of audiences
Detailed Assessment Description
This course includes weekly formative activities, such as online discussion questions and class discussions, designed to reinforce the concepts learned each week. These activities encourage students to stay engaged with the course materials, helping them identify areas for improvement and enhancing their overall learning experience.
Weekly Discussion assess: PLO1, PLO2, PLO3
BCom students: myBCom course points for PLO2
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Group Project
Assessment Overview
The group project provides a comprehensive learning experience by allowing you to tackle real industry challenges in the field of business analytics, supported by peers, academics, and industry practitioners. This hands-on, cross-disciplinary project not only aids in developing vital skills such as teamwork, communication, and problem-solving but also promotes collaboration when tackling complex issues in the real world.
Group Project assess: PLO1, PLO2, PLO3, PLO4, PLO7
BCom students: myBCom course points for PLO4
Detailed Assessment Description
The group project provides a comprehensive learning experience by allowing you to tackle real industry challenges in the field of business analytics, supported by peers, academics, and industry practitioners. This hands-on, cross-disciplinary project not only aids in developing vital skills such as teamwork, communication, and problem-solving but also promotes collaboration when tackling complex issues in the real world.
Group Project assess: PLO1, PLO2, PLO3, PLO4, PLO7
BCom students: myBCom course points for PLO4
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Individual Assignment
Assessment Overview
In the individual assessment task, you are expected to analyse a dataset using R or Python, with an emphasis on practical business analytics. Your objective is to develop authentic outputs, which may include dashboards, websites, or other interactive visualisations. Your communication abilities will be evaluated through a video presentation and/or a descriptive report that highlights your findings, insights, and the effectiveness of your devised solution. This task aims to enhance your problem-solving skills in real-world scenarios.
Individual Assignment assess: PLO1, PLO2, PLO3
BCom students: myBCom course points for PLO3
Course Learning Outcomes
- CLO2 : Critically evaluate quantitative analytics models
- CLO3 : Apply quantitative analytics tools and techniques to a range of business problems
- CLO5 : Communicate business analytics results and insights effectively to a variety of audiences
Detailed Assessment Description
In the individual assessment task, you are expected to analyse a dataset using R or Python, with an emphasis on practical business analytics. Your objective is to develop authentic outputs, which may include dashboards, websites, or other interactive visualisations. Your communication abilities will be evaluated through a video presentation and/or a descriptive report that highlights your findings, insights, and the effectiveness of your devised solution. This task aims to enhance your problem-solving skills in real-world scenarios.
Individual Assignment assess: PLO1, PLO2, PLO3
BCom students: myBCom course points for PLO3
General Assessment Information
As a student at UNSW you are expected to display academic integrity in your work and interactions. Where a student breaches the UNSW Student Code with respect to academic integrity, the University may take disciplinary action under the Student Misconduct Procedure. To assure academic integrity, you may be required to demonstrate reasoning, research and the process of constructing work submitted for assessment.
To assist you in understanding what academic integrity means, and how to ensure that you do comply with the UNSW Student Code, it is strongly recommended that you complete the Working with Academic Integrity module before submitting your first assessment task. It is a free, online self-paced Moodle module that should take about one hour to complete.
Grading Basis
Standard
Requirements to pass course
In order to pass this course students must:
- Achieve a composite mark of at least 50 out of 100
- Engage actively in course learning activities and attempt all assessment requirements
- Meet any additional requirements specified in the assessment details
- Meet the specified attendance requirements of the course
Course Schedule
| Teaching Week/Module | Activity Type | Content |
|---|---|---|
| Week 1 : 27 May - 2 June | Lecture |
Course Introduction Regression Chapter 7, 8 |
| Week 2 : 3 June - 9 June | Lecture |
Logistic Regression Chapter 9 |
| Week 3 : 10 June - 16 June | Lecture |
Forecasting with Time Series Data Chapter 10 |
| Week 4 : 17 June - 23 June | Lecture |
Supervised Data Mining: k-Nearest Neighbors and Naive Bayes Chapter 11, 12 |
| Week 5 : 24 June - 30 June | Lecture |
Supervised Data Mining: Decision Trees Chapter 13 |
| Week 6 : 1 July - 7 July | Module |
Flexibility Week |
| Week 7 : 8 July - 14 July | Lecture |
Unsupervised Data Mining Chapter 14 |
| Week 8 : 15 July - 21 July | Lecture |
Imbalanced Class Supplementary |
| Week 9 : 22 July - 28 July | Lecture |
Deep Learning Supplementary |
| Week 10 : 29 July - 4 August | Lecture |
Risk Analysis and Simulation Chapter 16 |
Attendance Requirements
Students are strongly encouraged to attend all classes and review lecture recordings.
General Schedule Information
Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit: https://student.unsw.edu.au/new-calendar-dates
Course Resources
Prescribed Resources
The website for this course is on Moodle.
The course will use various digital resources, but they all will be linked from Moodle.
To access the Moodle online support site for students, follow the links from that website to UNSW Moodle Support/Support for Students. Additional technical support can be obtained from [email protected] (02 9385 1333).
All course contents will be available from the course website. It is essential that you visit the site regularly to see any notices posted there by the course coordinator, as it will be assumed that they are known to you within a reasonable time.
Textbooks
There are many books of relevance to the course topics. The following book will be the main text references for a substantial part of the course:
Jaggia, Sanjiv, Alison Kelly, Kevin Lertwachara and Leida Chen. Business Analytics: Communicating with Numbers 2/e. McGraw-Hill Education, 2022
Additional readings from the professional actuarial literature will also be used to provided additional context, details, and examples. This will be communicated in the course website.