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Unit outline_

BUSS6002: Data Science in Business

Semester 1, 2023 [Normal day] - Remote

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
? 
None
Corequisites
? 
QBUS5001 or QBUS5002
Prohibitions
? 
None
Assumed knowledge
? 

Basic knowledge of probability and statistics

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Stephen Tierney, stephen.tierney@sydney.edu.au
Lecturer(s) Fabian Held, fabian.held@sydney.edu.au
Stephen Tierney, stephen.tierney@sydney.edu.au
Manoj Thomas, manoj.thomas@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Closed book, comprehensive examination.
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO6 LO5 LO7
Assignment Individual Assignment 1
Written and programming task
20% Week 07
Due date: 07 Apr 2023 at 23:59
500 - 1000 words (indicative only)
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
Assignment Individual Assignment 2
Written and programming task.
20% Week 12
Due date: 19 May 2023 at 23:59
2000 words (indicative only).
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Small continuous assessment Weekly Quiz
Quiz accompanies weekly lecture material
10% Weekly Variable
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2

Assessment summary

Weekly Quiz: There will be a total of 10 quizzes throughout the semester on a roughly weekly basis. These quizzes will assess your understanding of the weeks lecture and tutorial. Refer to canvas for the schedule.

Individual Assignments: The assignments will require you to carry out applied data science projects, which typically includes exploratory analysis, visualisation, statistical analysis and modelling. These assignments will require students to complete written and programming components.

Final Exam: The final exam will assess all aspects of the unit from the entire semester. The exam questions will be a mix of multiple choice and short answer.

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.

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 Data Science Capabilities Lecture (2 hr) LO1 LO5 LO6
Python Fundamentals 1 Tutorial (2 hr) LO1 LO2 LO4
Week 02 Data Lifecycle Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Python Fundamentals 2 Tutorial (2 hr) LO1 LO2 LO4
Week 03 Exploratory Analysis Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Data Wrangling 1 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Week 04 Mathematical Foundations and Scientific Computing Lecture (2 hr) LO1 LO2 LO3 LO4
Data Wrangling 2 and Visualisation 1 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Week 05 Cluster Analysis Lecture (2 hr) LO2 LO3 LO4
Visualisation 2 and Clustering Tutorial (2 hr) LO1 LO2 LO3 LO4 LO7
Week 06 Regression Lecture (2 hr) LO2 LO3 LO4 LO6 LO7
Exploratory Analysis Reflection Tutorial (2 hr) LO1 LO2 LO3 LO4 LO7
Week 07 Feature Engineering Lecture (2 hr) LO2 LO3 LO4 LO6 LO7
Regression Tutorial (2 hr) LO2 LO3 LO4 LO7
Week 08 Classification Lecture (2 hr) LO2 LO3 LO4 LO7
Feature Engineering Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Week 09 Model Evaluation Lecture (2 hr) LO2 LO3 LO4
Classification Tutorial (2 hr) LO2 LO3 LO4
Week 10 Optimisation Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Model Evaluation Tutorial (2 hr) LO2 LO3 LO4 LO6 LO7
Week 11 Big Data Solutions Lecture (2 hr) LO2 LO3 LO4 LO5 LO7
Optimisation Tutorial (2 hr) LO2 LO3 LO4
Week 12 Assignment Focus Session Lecture (2 hr) LO4 LO5 LO6 LO7
Optimisation Tutorial (2 hr) LO2 LO3 LO4
Week 13 Revision Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Revision Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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 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. Identify types and sources of data, data quality issues and interact with data storage systems.
  • LO2. Explain and apply foundational techniques of data analysis to business problems.
  • LO3. Categorise business problems in order to select appropriate data analysis techniques and tools.
  • LO4. Interpret and evaluate the outputs of data analysis techniques and tools.
  • LO5. Evaluate data science capabilities of businesses and apply data science process models.
  • LO6. Identify and assess ethical issues associated with data driven decision making.
  • LO7. Communicate effectively with technical and non-technical audiences.

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.

No changes have been made since this unit was last offered.

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.