QBUS5001: Semester 1, 2022

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Units QBUS5001 Semester 1 2022 [Normal day]

Unit of study_

QBUS5001: Foundation in Data Analytics for Business

Semester 1, 2022 [Normal day] - Remote

Overview

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

Unit details and rules

Unit code	QBUS5001
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	ECMT5001 or QBUS5002
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Students should be capable of reading data in tabulated form and working with Microsoft EXCEL and doing High School level of mathematics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Anastasios Panagiotelis, anastasios.panagiotelis@sydney.edu.au
Lecturer(s)	Simon Loria, simon.loria@sydney.edu.au
	Yves Tam, yves.tam@sydney.edu.au
	Anastasios Panagiotelis, anastasios.panagiotelis@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam n/a	45%	Formal exam period	2 hours
Outcomes assessed:
Small continuous assessment	Quizzes n/a	10%	Multiple weeks	n/a
Outcomes assessed:
Assignment	Assignments Assignments n/a	25%	Multiple weeks	n/a
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam Closed book exam.	20%	Week 08 Due date: 11 Apr 2022 at 12:00	1 hour
Outcomes assessed:
= hurdle task ? = Type B final exam ? = Type B in-semester exam ?

Assessment summary

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 sydney.edu.au/students/guide-to-grades.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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	Theme 1 (All about data): Data fundamentals; Exploratory analysis - Summary Statistics	Lecture and tutorial (4 hr)
Week 02	Theme 1 (All about data): Exploratory data analysis - Visualization; Data communication	Lecture and tutorial (4 hr)
Week 03	Theme 2 (All about probability): Probability interpretation; Probability definitions	Lecture and tutorial (4 hr)
Week 04	Theme 2 (All about probability): Probability and decisions; Monte Carlo example	Lecture and tutorial (4 hr)
Week 05	Theme 2 (All about probability): Statistics - the bridge between probability and data; Estimation and confidence intervals	Lecture and tutorial (4 hr)
Week 06	Theme 3 (All about models): Simple regression- motivation and details	Lecture and tutorial (4 hr)
Week 07	Theme 3 (All about models): Simple regression - using logs, dummy variables and model comparison	Lecture and tutorial (4 hr)
Week 08	Theme 3 (All about models): Multiple regression I	Lecture and tutorial (4 hr)
Week 09	Theme 3 (All about models): Multiple regression II	Lecture and tutorial (4 hr)
Week 10	Theme 4 (All about inferences): Hypothesis testing for single population	Lecture and tutorial (4 hr)
Week 11	Theme 4 (All about inferences): Hypothesis testing for multiple populations	Lecture and tutorial (4 hr)
Week 12	Theme 4 (All about inferences): Hypothesis testing in regression	Lecture and tutorial (4 hr)

Attendance and class requirements

There are three parts to the unit:

Self-paced study modules on Canvas website
Zoom Q&A on Mondays, 11 to 1
Tutorials (see your schedule).

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

Selvanathan, E. A., Selvanathan, S and Keller, G. (2021) Business Statistics, Australia and New Zealand 8th Edition. Cengage Learning, Australia

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. build a strong quantitative skill set for business decision making; create statistical models for studying relationship amongst business variables; demonstrate proficiency in the use of statistical software for quantitative modelling
LO2. evaluate underlying theories, concepts, assumptions and arguments in business related fields
LO3. identify problems within real-world constraints and collect data for decision making; manage, analyse, evaluate and use information efficiently and effectively; demonstrate coherent arguments when recommending solutions
LO4. communicate confidently and coherently to a professional standard both orally and in writing
LO5. defend data integrity; analyse data and report results professionally and ethically.

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.

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

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