This course builds on the material introduced in introductory Business Information Systems courses by covering how business analytics and business intelligence can be used for improved business decision-making. Contemporary forms of analytics such as visual, text, sentiment, web, and social are covered in the course, as well as established technologies like decision support, knowledge management, collaborative and expert systems.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. identify the business problems that require decision-making support from business analytics
2. establish the best search strategy to acquire evidence relevant to the business problem
3. establish the business analytics method relevant to the business process and the reliability and validity of evidence
4. summarise the relevant evidence in view of finding analytics solutions to business questions
5. recognise social and ethical implications of analytics solutions to the business problem
6. design optimal analytics processes to increase the likelihood of favourable business decision-making outcomes
7. reflect on feedback to adjust solutions.

Research-Led Teaching

Lectures and tutorials will be done in-class in-person and recorded on ECHO 360.

The course has readings that are a mix of research and industry publications that cover both theoretical concepts and practical application of the content. The individual assessment provides the opportunity for students to apply newly developed skills and receive timely feedback. The visualisation and major assignments allow the student to apply their cumulative analytical research skills and decision-making knowledge to a real-world scenario of their choosing.

Field Trips

There are no field trips in this course.

Additional Course Costs

There are no additional class costs expected in this course. The software packages used are available on a trial or free of charge basis.

Examination Material or equipment

There are no examinations in this course.

Required Resources

There are no additional required resources in this courses, but access to a modern computing device (tablet, laptop or desktop computer) is highly advisable. The ANU has a number of computer labs spread across the campus (map)

Recommended Resources

Students are strongly encouraged to carefully read the weekly (pre)reading materials provided on Canvas from Week 1 onwards.

Suggested textbook (Hardcopy available in ANU Library reserve & short loan collection, as well as Online )

The 11th edition of this book, Analytics, data science, & artificial intelligence : systems for decision support  is available in the library catalogue: https://anu.primo.exlibrisgroup.com/permalink/61ANU_INST/1uq45vd/alma991007848339707631

Title: Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Global Edition (Book)

Autor: Sharda & Delen & Turban

Edition: 11

Staff Feedback

Students will be given feedback in the following forms in this course:

• Written comments
• Verbal comments
• Feedback to the whole class, to groups, to individuals, focus groups

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Scaling

Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.

Support for students

The University offers a number of support services for students. Information on these is available online from https://www.anu.edu.au/students/student-life

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Week 1: BA Overview & DM Foundations	No tutorials in Week 1
2	Week 2: Descriptive AnalyticsData Warehousing
3	Week 3: Descriptive AnalyticsBusiness Reporting, Visual Analytics and Business Performance Management
4	Week 4: Predictive AnalyticsData Mining & Techniques for Predictive Modelling	Individual Assessment 1 - Due Friday 11.59 PM Week 4
5	Week 5: Predictive AnalyticsText Analytics, Text Mining, and Sentiment Analysis
6	Week 6: Predictive AnalyticsWeb Analytics, Web Mining, and Social Analytics	Assessment 2 - Visualisation report - Due Friday 11:59 PM Week 6
7	Week 7: Prescriptive AnalyticsModel-Based Decision Making: Optimisation and Multi-Criteria Systems
8	Week 8: Prescriptive AnalyticsModelling and Analysis: Heuristic Search Methods and Simulation
9	Week 9: Prescriptive AnalyticsAutomated Decision Systems and Expert Systems
10	Week 10: Prescriptive AnalyticsKnowledge Management and Collaborative Systems
11	Week 11: Big Data
12	Week 12: Emerging Trends & Future Impact
13	No teaching / End of Semester Examination Period	Major Assignment (Final Report) - Due first Friday 11:59 PM in end of semester examination period.

Tutorial Registration

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

This course has tutorials or tutorial-like teaching activities. Further details about the structure and teaching activities for this course will be available on the course Canvas site at the start of O-Week.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Individual Assessment - 1 (Individual - 25%)	25 %	20/03/2026	31/03/2026	1,2,3,4,5,6
Visualisation Report Assignment (Individual - 35% - HURDLE)	35 %	02/04/2026	20/04/2026	1,2,3,4,5,6
Major Final Report Assignment (Individual - 40% - HURDLE)	40 %	05/06/2026	02/07/2026	1,2,3,4,5,6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Policy and General Information
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

The lectures for this course will be delivered Face-to-Face. Participation is expected in all workshop classes but not assessed.

Additional opportunities for group or on-to-one consultation with the convenor will be communicated via the course site in Week 1.

Attendance at seminars, lectures, and tutorials, while not compulsory, is expected in line with "Code of Practice for Teaching and Learning," Clause 2 paragraph (b). Where students will not be able to attend a seminar, lecture and tutorial, they should advise the Convenor and discuss how to otherwise address the learning materials.

ASSESSMENT HURDLE: Assessment Task 2 and Assessment Task 3 must be attempted and submitted to be eligible for a passing final course grade. This is an assessment hurdle in line with the student assessment coursework policy (see https://policies.anu.edu.au/ppl/document/ANUP_004603). Assessments must be attempted and submitted so that an appropriate evaluation of the students' understanding and application of the learning objectives.

Examination(s)

There are no examinations for this course.

Assessment Task 1

Individual Assessment - 1 (Individual - 25%)

Due Date

The assessment work comprises 25% of your final course mark. A set of questions related to materials from weeks 1-3 will be assigned, and you are required to attempt to answer these questions individually and submit your answers to Canvas by the due date.

Due by the end of 11:59 pm Friday, Week 4.

Feedback and comments will be provided by 31 March 2026.

Assessment Type

Individual

Form of Submission

Assessment tasks are to be submitted using the course Canvas site by the deadline. Submitted assessment does not require a cover sheet. Please keep a copy of the submitted work for your records.

Note that your answers must be provided in word-processed or other appropriate electronically produced form. Unless specifically noted in the instructions, handwritten work will not be accepted or, if submitted, will not be marked.

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assessment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin, please visit ANU Online.

Performance Level

Note that individual questions will not be marked. Nor should you expect there to be detailed written comments on your submitted work because you are expected to obtain this kind of feedback for yourself in private consultation or during the discussion in workshop classes.

Once the assessment has been marked (typically before the workshop in which the answers will be discussed or 1-2 days after), results will be released via the gradebook feature on Canvas.

There is no hard word limit for this Assessment, but the suggested length of the response is 3-4 pages (no penalty for exceeding it), depending on the question(s) set. No late submissions for this task. Submission of assessment tasks without an extension after the due date is not permitted, and a mark of 0 will be awarded. More information about the assessments will be made available on Canvas.

Students must also submit a reflection explaining how and why they chose to use—or not to use—generative AI tools and other AI-based tools in the completion of this assessment.

Use of Artificial Intelligence (AI): AI use is rapidly growing in all sectors, particularly the use of Large Language Models, of which there are many proprietary brands. For Assessment 1, students may choose to use or not use generative AI tools (e.g. GPT-4, DALL-E, Copilot) and other tools (e.g. Grammarly). In any case where AI tools are used, the student must do so in a way consistent with the ANU Academic Integrity principles for use of GenAI, as well as accurately cite and reference what tools were used, and explain in the Reflection section of Assessment 1, how and why the tools were used, or why they were not used. The students need to clearly acknowledge the use of AI in the relevant parts of the assessment task.

Guidance on how to do this appropriately is provided in the assessment requirements on the course Canvas page, and the ANU provides further broad guidance in the

ANU Gen AI LibGuide. As part of handling a potential breach of academic integrity, students are reminded that they may be requested to meet with the Convenor to discuss any assessment submission, including responding to questions on the content of submissions and their understanding of the course concepts assessed by the submission.

Submit the deliverable in a format that preserves ‘tracked changes’ (e.g. MS Word, Apple Pages, or similar) that shows the progression of academic effort and contribution towards completing the task.

Marking Criteria

More information about this assignment will be made available, and a marking rubric will be provided on Canvas at least two weeks before the due date.

Late submissions are not allowed for this assessment task.

Marks and limited feedback are normally made available before the workshop, in which the answers will be discussed (timetable dependent).

Assessment Task 2

Visualisation Report Assignment (Individual - 35% - HURDLE)

Task 2 and Task 3, which are sequential, together form 75% of the total grade.

The visualisation report (task 2) has to be completed before task 3 (report writing). They may be related, though they do not have to be related.

Task 2 focuses on the visualisation part of how data visualisation can be used by real-life organisations.

It consists, essentially, of you, the student, developing one (or more) case studies, and you should regard it as offering both an educational experience and an opportunity for you to demonstrate that you have mastered the various techniques and tools covered in the course.

You, the student, are responsible for choosing what will be the target organisation(s) for the two tasks comprising this assignment. It is suggested that your choice relates to hobbies, work or other interests for which they already possess the necessary information and knowledge or can readily gain access to it from experts. It is therefore best both from the point of view of learning opportunities as well as getting the best marks to choose a case study target that is real (i.e. relates to a genuine problem, opportunity or need in the real world) rather than one that is entirely fictional (i.e. dreamed up wholly within your own head).

For Task 2, the Visualisation Report, you are to include a small report (~ 8 pages in length) demonstrating how data visualisation can be used by a real-life organisation. Text exceeding the page count will not be read.

Assessment Type

Individual

Due Date

Due no later than the end of 11:59 pm Thursday of Week 6. There is a 5% penalty for each late working day for late submissions.

Feedback and comments will be provided within 10 working days of submission

Form of Submission

Assignments are to be submitted using the course site. Submitted assessment does not require a cover sheet, but most use a professional report format that includes a title page containing the student's name and number. Please keep a copy of the submitted work for your records.

Note that your work must be provided in a word-processed or other appropriate electronically produced form. Unless specifically noted in the instructions, handwritten work will not be accepted or, if submitted, will not be marked.

The ANU is using Turnitin to enhance student citation and referencing techniques and to assess assessment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit ANU Online.

Use of Artificial Intelligence (AI): AI use is rapidly growing in all sectors, particularly the use of Large Language Models, of which there are many proprietary brands. For Assessment 2, students may choose to use or not use generative AI tools (e.g. GPT-4, DALL-E, Copilot) and other tools (e.g. Grammarly). In any case where AI tools are used, the student must do so in a way consistent with the ANU Academic Integrity principles for use of GenAI, as well as accurately cite and reference what tools were used, and explain in the Reflection section of Assessment 2, how and why the tools were used, or why they were not used. The students need to clearly acknowledge the use of AI in the relevant parts of the assessment task.

Guidance on how to do this appropriately is provided in the assessment requirements on the course Canvas page, and the ANU provides further broad guidance in the

ANU Gen AI LibGuide. As part of handling a potential breach of academic integrity, students are reminded that they may be requested to meet with the Convenor to discuss any assessment submission, including responding to questions on the content of submissions and their understanding of the course concepts assessed by the submission.

Submit the deliverable in a format that preserves ‘tracked changes’ (e.g. MS Word, Apple Pages, or similar) that shows the progression of academic effort and contribution towards completing the task.

Mandatory Assessment

This is a hurdle assessment in line with the student assessment coursework policy (see https://policies.anu.edu.au/ppl/document/ANUP_004603). You must submit both Assessment Task 2 and Assessment Task 3 to be eligible to pass the course.

Assessment Task 3

Major Final Report Assignment (Individual - 40% - HURDLE)

This is the second instalment, sequential to assessment task 2. Task 3 carries 40% weight in the final grade for this course.

This is the final report you will need to produce (in a case study format) following the visualisation report in assessment task 2.

The two assessment tasks can be linked (but do not need to be) if you wish the major final report (task 3) to go into further detail about what you report in the visualisation assignment (task 2).

You, the student, are responsible for choosing what will be the target organisation(s) for your two assignments. It is suggested that your choice relates to hobbies, work or other interests for which you already possess the necessary information and knowledge or can readily gain access to it from experts. It is therefore best both from the point of view of learning opportunities as well as getting the best marks to choose a case study target that is real (i.e. relates to a genuine problem, opportunity or need in the real world) rather than one that is entirely fictional (i.e. dreamed up wholly within your own head).

The major final report will be in case study format, summarising how business analytics can be applied by a real-life organisation. (Length ~ 20 pages of text (body of report – excludes front and end matter)). Text exceeding the page count will not be read.

Further details about the task, including the marking criteria, will be provided on Canvas two weeks before Week 1 of the semester.

Due Date

Due no later than 11.59 pm Friday, 5th June, in the first week of the exam period. There is a 5% penalty for each late working day for late submissions.

Assessment Type

Individual

Feedback and comments will be provided upon release of the final results

Form of Submission

Assignments are to be submitted using the course Canvas site. Submitted assessment does not require a cover sheet, but must use a professional report format that includes a title page containing the student's name and number. Please keep a copy of the submitted work for your records.

Note that your work must be provided in a word-processed or other appropriate electronically produced form. Unless specifically noted in the instructions, handwritten work will not be accepted or, if submitted, will not be marked.

The ANU is using Turnitin to enhance student citation and referencing techniques and to assess assessment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit ANU Online.

Use of Artificial Intelligence (AI): AI use is rapidly growing in all sectors, particularly the use of Large Language Models, of which there are many proprietary brands. For Assessment 3, students may choose to use or not use generative AI tools (e.g. GPT-4, DALL-E, Copilot) and other tools (e.g. Grammarly). In any case where AI tools are used, the student must do so in a way consistent with the ANU Academic Integrity principles for use of GenAI, as well as accurately cite and reference what tools were used, and explain in the Reflection section of Assessment 3, how and why the tools were used, or why they were not used. The students need to clearly acknowledge the use of AI in the relevant parts of the assessment task.

Guidance on how to do this appropriately is provided in the assessment requirements on the course Canvas page, and the ANU provides further broad guidance in the

ANU Gen AI LibGuide. As part of handling a potential breach of academic integrity, students are reminded that they may be requested to meet with the Convenor to discuss any assessment submission, including responding to questions on the content of submissions and their understanding of the course concepts assessed by the submission.

Submit the deliverable in a format that preserves ‘tracked changes’ (e.g. MS Word, Apple Pages, or similar) that shows the progression of academic effort and contribution towards completing the task.

Mandatory Assessment

This is a hurdle assessment in line with the student assessment coursework policy (see https://policies.anu.edu.au/ppl/document/ANUP_004603). You must submit both Assessment Task 2 and Assessment Task 3 to be eligible to pass the course.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Learning Platforms website.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Refer to the details for each assessment item. Policy regarding late submission (where applicable) is detailed below:

• Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.
• Late submission permitted. Late submission of assessment tasks without an approved extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

All requests for Assessment Adjustment (including Requests for Extension and for Consideration of Extenuating Circumstances) should be submitted via ANUHub .

Referencing Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Canvas’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Returning Assignments

Please see relevant assessment task details above.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Unless specified otherwise in the assignment requirements, resubmissions are permitted up until the due date and time, but not allowed afterwards.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students