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Unit outline_

INFO5060: Data Analytics and Business Intelligence

Intensive June - July, 2023 [Block mode] - Camperdown/Darlington, Sydney

The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Basic knowledge of information systems as covered in COMP5206 or ISYS2160 (or equivalent UoS from different institutions)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Nasim Ahmed, nasim.ahmed@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
hurdle task
Final Exam
Final examination: on-campus; closed book and timed
50% Formal exam period
Due date: 17 Jul 2023 at 13:00
2 hours
Outcomes assessed: LO1 LO2 LO4
Small test Daily Quizzes
4x online quizzes based on topics covered in each teaching day
10% Multiple weeks ~10 minutes each
Outcomes assessed: LO1 LO2 LO4
Assignment group assignment Dashboard solution I
Initial submission of a Business Intelligence dashboard
10% Week 02
Due date: 30 Jun 2023 at 23:59

Closing date: 03 Jul 2023
N/A
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Presentation group assignment BI Solution Presentation
Pre-recorded video presentation on your group's BI solution
10% Week 03 10 minutes
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Assignment group assignment Presentation Peer Review
Provide peer review feedback on a paired Presentation
5% Week 03
Due date: 09 Jul 2023 at 23:59

Closing date: 12 Jul 2023
N/A
Outcomes assessed: LO2 LO3 LO4 LO5 LO6
Assignment group assignment Dashboard solution II
Revised and expanded submission of a Business Intelligence dashboard
15% Week 04
Due date: 15 Jul 2023 at 23:59
N/A
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
hurdle task = hurdle task ?
group assignment = group assignment ?

Assessment summary

 

  • Dashboard Solution: Your group will develop a business intelligence dashboard over the course of the unit, progressively adding capabilities to it over 2 main submissions.
  • BI Solution Presentation: Your group will present your initial dashboard in a video to highlight its capabalities and explain its intended role within your scenario’s organisation.
  • Presentation Peer Review: Your group will be paired with another group, and will review the presentation from your paired group to provide questions and feedback to assist their work on the second submission.
  • Daily Quizzes: The first 5 of 6 teaching days will have an associated basic knowledge and understanding quiz undertaken by the end of the following day, conducted online in Canvas.
  • Final Exam: The final exam will consist of multiple choice and essay style questions and be a duration of 2hours.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard. 

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. A student must also achieve an overall final mark of 50 or more.

Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Daily quizzes, BI solution presentation, and Presentation peer review all may not accept late submissions. Daily quizzes may not be submitted late as answers are auto-released after the due time. The BI solution presentation must be submitted in time for your peer review pair group to review your work. The peer review also has a strict due time to provide timely feedback to the target group. Dashboard submissions I and II may be accepted late with a 5% per calendar day penalty up to 10 days - however feedback for submission I may not be available in time for submission II if this is submitted late.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Course Overview & Admin Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Introduction to Business Intelligence & Descriptive Analysis I Lecture (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Course Overview & Admin Practical (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Introduction to Business Intelligence & Descriptive Analysis I Practical (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 Descriptive Analysis II Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Predictive Analytics I & Project discussion Lecture (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Descriptive Analysis II Practical (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Predictive Analytics I & Project discussion Practical (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Project discussion & Predictive Analytics II Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Predictive Analytics II & Analytics at Scale Lecture (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Project discussion & Predictive Analytics II Practical (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Predictive Analytics II & Analytics at Scale Practical (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 04 Analytics at Space & Evaluation Success Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Unit review Lecture (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Analytics at Space & Evaluation Success Practical (2.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Unit review Practical (2 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

Classes for Intensive July 2023 will be on Thursdays and Fridays over four weeks. Each week, there will be four in-person sessions:

  • Lecture 1: Thursdays 10.00am-12.00pm
  • Lecture 2: Thursdays 1.00pm -3.30pm
  • Prac 1: Thursdays 3.30pm-6.00pm
  • Prac 2: Fridays 4.00pm-6.00pm

The final in-person exam will be on Monday 17 July, 10.00am-1.00pm.

Class dates for 2023

Week 1

Thursday 22 June

Friday 23 June

Week 2

Thursday 29 June

Friday 30 June

Week 3

Thursday 6 July

Friday 7 July

Week 4

Thursday 13 July

Friday 14 July

Week 5

Final Exam: Monday 17 July 10.00am-1.00pm

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Design and implement a business intelligence dashboard solution
  • LO2. Demonstrate thorough understanding of the conceptual foundations and technological underpinnings of data analytics and components of business intelligence architecture
  • LO3. Use library databases and search online material
  • LO4. Provide professional decision-making in developing a business intelligence solution. Exercise sound critical judgement in undertaking a real world Business Intelligence development case study.
  • LO5. Contribute to team work through the team assignments and presentations.
  • LO6. Present in-depth on a `customer` on a Business Intelligence Solution. Extensive consideration of theoretical and methodological issues regarding the solution proposed. Interpret and discuss issues and situations around the solution with due consideration of broad theoretical/practical context.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Unit scheduling has been adjusted to allow more focus on quality assessment work.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.