Outline

Overview

Growing volumes of data and, more importantly, the computation power to analyse it are now widely recognised as key business assets. No single discipline has the tools to make the most of these assets. Instead successful "big data" capability requires (a) the ability to understand how data can (and often cannot) be used to generate new insights into substantive problems (b) knowledge of how data are generated and used and (c) the ability to understand connections between variables captured in data. This unit provides an overview of principles from the disciplines of Business Information Systems and Business Analytics, applied in the context of Marketing problems, relevant for using 'big data' in business planning, decision-making and operations.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	QBUS5001 or QBUS5002
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic knowledge of statistics, probability and linear algebra
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jie Yin, jie.yin@sydney.edu.au
Lecturer(s)	Fabian Held, fabian.held@sydney.edu.au
	Manoj Thomas, manoj.thomas@sydney.edu.au
	Stephen Tierney, stephen.tierney@sydney.edu.au
	Jie Yin, jie.yin@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final exam n/a	50%	Formal exam period	1.5 hours
Outcomes assessed:
In-semester test	Mid-semester exam MCQ	25%	Week 06	1 hour
Outcomes assessed:
Assignment	Assessment 1 Written and programming task	25%	Week 13 Due date: 03 Jun 2020 at 12:00 Closing date: 10 Jun 2020	20-25 pages
Outcomes assessed:
= group assignment ?

Assessment summary

Assessment 1: Students will complete this group assignment in a group (of a maximum of 3 members). This assignment will test students’ knowledge that they learnt from the whole course. Students need to analyse some real world data sets provided and report analytical results to give business decision making suggestions.
Mid-semester exam: Comprehensive exam that covers topics that students learnt from weeks 1-5. The exam questions will be multiple-choice questions.
Final exam: The final exam will assess all aspects of this unit from weeks 1-13. It will primarily test how familiar the students are with all the materials including some programming basics covered in this unit. The exam questions will be a mixture of multiple-choice questions, conceptual questions, and derivations.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Data science fundamentals 1	Lecture (2 hr)
Week 01	Introduction to Python	Tutorial (2 hr)
Week 02	Data science fundamentals 2	Lecture (2 hr)
Week 02	Data and visualisation 1	Tutorial (2 hr)
Week 03	Data science fundamentals 3	Lecture (2 hr)
Week 03	Data and visualisation 2	Tutorial (2 hr)
Week 04	Representation of data and modelling paradigms	Lecture (2 hr)
Week 04	Data and visualisation 3	Tutorial (2 hr)
Week 05	Predictive analytics 1	Lecture (2 hr)
Week 05	Databases	Tutorial (2 hr)
Week 07	Predictive analytics 2	Lecture (2 hr)
Week 07	Linear regression	Tutorial (2 hr)
Week 08	Model evaluation and selection techniques	Lecture (2 hr)
Week 08	logistic regression	Tutorial (2 hr)
Week 09	Computing techniques for Big Data 1	Lecture (2 hr)
Week 09	Model selection	Tutorial (2 hr)
Week 10	Computing techniques for Big Data 2	Lecture (2 hr)
Week 10	Text analytics	Tutorial (2 hr)
Week 11	Big Data in marketing and text analytics	Lecture (2 hr)
Week 11	Optimisation and subsampling	Tutorial (2 hr)
Week 12	Unsupervised learning and application	Lecture (2 hr)
Week 12	MapReduce	Tutorial (2 hr)
Week 13	Customer analytics	Lecture (2 hr)
Week 13	Clustering	Tutorial (2 hr)

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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

All readings for this unit can be accessed through Library eReserve, available on Canvas.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. understand the importance of cross-disciplinary knowledge in solving business problems using business data
LO2. understand the opportunities and challenges when working with Big Data
LO3. use the state-of-the-art data analysis techniques and tools for Big Data
LO4. understand how the principles and tools from business information systems and business analytics can be applied in the context of marketing problems
LO5. work productively and collaboratively in a team.

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
Learning outcomes

Responding to student feedback

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

Individual assignment is no longer part of assessment for this unit.

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BUSS6002: Data Science in Business

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect