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Unit of study_

INFO5060: Data Analytics and Business Intelligence

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.

Details

Academic unit Computer Science
Unit code INFO5060
Unit name Data Analytics and Business Intelligence
Session, year
? 
Intensive July, 2020
Attendance mode Block mode
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
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None
Corequisites
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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

No

Teaching staff and contact details

Coordinator Simon Poon, simon.poon@sydney.edu.au
Lecturer(s) Khimji Vaghjiani , khimji.vaghjiani@sydney.edu.au
Tutor(s) Harshana Randeni Kadupitige , sidath.randenikadupitige@sydney.edu.au
Type Description Weight Due Length
Final exam Final Exam
N/A
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2
Presentation group assignment Case Studies
N/A
10% Multiple weeks N/A
Outcomes assessed: LO1 LO5 LO4 LO2
Online task In-Tutorial Assignments
N/A
10% Multiple weeks Completed during Tutorial
Outcomes assessed: LO1 LO2 LO4
Assignment group assignment Final Project and Presentation
N/A
20% Week -04 15 minutes
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Assignment group assignment Data Analytics Simulation
N/A
10% Week 02 N/A
Outcomes assessed: LO1 LO5 LO4 LO2
group assignment = group assignment ?

 

  • Team Assignment: The team assignment will require students to apply the knowledge and techniques covered in the course to develop a business intelligence solution based on a real world case study. Using an experiential learning approach, each team will use a Business Intelligence methodology to gather business requirements, design a solution and build a working prototype of a performance dashboard. To make the learning dynamic, the lecturer will role play the customer and provide information for the business requirements, and ongoing feedback as the teams’ design and build their solutions. There will be a strong focus on leveraging the industry expertise of the lecturer to coach the teams through the process and soft skills of building a business intelligence solution. The assessment deliverables will be based on both the BI Solution and a presentation.
  • 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.

For more information see sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 1. Course overview; 2. Introduction (Chapter 1, Sharda et al. 2018); 3. Descriptive Analytics I (Chapter 2) Lecture (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
1. Tableau; 2. Introduction to Data Analytics Simulation Tutorial (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 1. Descriptive Analytics II (Chapter 3) Lecture (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
1. Tableau Prep; 2. Assessment: Data Analytics Simulation - Students to choose and present in Tutorial Tutorial (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 1. Predictive Analytics I (Chapter 4); 2. Predictive Analytics II (Chapter 5); 3. Prescriptive Analytics (Chapter 6) Lecture (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
1. Data Mining & simulation; 2. Assessment: Introduction to Case Study #1: Analytics Case Study 1 presentation in Tutorial Tutorial (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 04 1. Big Data Analytics (Chapter 7) Lecture (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
1. Assessment: Introduction to Case Study #2: Student Choice 2 presentation in Tutorial; 2. Social Analytics; 3. Assessment: Final Project Preparation Tutorial (4.5 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

Date / Time

Topics

Week 1

Monday June 29th (6pm-9pm) Lecture + Tutorial (1.5hr each)

Thursday July 2nd  (6pm-9pm) Lecture + Tutorial

Saturday  July 4th  (9am-12pm) Lecture + Tutorial

Guest speaker to TBC

Course overview

Introduction (Chapter 1, Sharda et al. 2018)

Descriptive Analytics I (Chapter 2)

Tutorial week #1: Tableau

Introduction to Data Analytics Simulation

Week 2

Monday July 6th (6pm-9pm) Lecture  + Tutorial

Thursday July 9th  (6pm-9pm) Lecture + Tutorial

Saturday  July 11th  (9am-12pm) Lecture + Tutorial

Guest speaker to TBC

Descriptive Analytics II (Chapter 3)

Assessment: Data Analytics Simulation – Students to choose and present in Tutorial

Tutorial week #2: Tableau Prep

Week 3

Monday July 13th (6pm-9pm) Lecture + Tutorial

Thursday July 16th  (6pm-9pm) Lecture + Tutorial

Saturday  July 18th  (9am-12pm) Lecture + Tutorial

Guest speaker to TBC

Predictive Analytics I (Chapter 4)

Tutorial week #3 & 4 : Data Mining & simulation

Assessment: Introduction to Case Study #1: Analytics Case study 1 presentation in Tutorial

Predictive Analytics II (Chapter 5)

Prescriptive Analytics (Chapter 6)

Week 4

Monday July 20th (6pm-9pm) Lecture  + Tutorial

Thursday July 23rd  (6pm-9pm) Lecture + Tutorial

Saturday  July 25th  (9am-12pm) Lecture + Tutorial

Guest speaker to TBC

Assessment Introduction to Case Study #2: Student Choice 2 presentation in Tutorial

Big Data Analytics (Chapter 7)

Tutorial week #4: Social Analytics

Assessment Final Project Preparation

TBC

Online (Final Exam)

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.

Required readings

Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, by Ramesh Sharda, Dursun Delen, and Efraim Turban, 2018, Pearson https://bit.ly/2TXYwXw 

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 unit has been adjusted for online delivery.

Disclaimer

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