PMGT5866: Semester 2, 2023

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Units PMGT5866 Semester 2 2023 [Normal evening]

Unit outline_

PMGT5866: Quantitative Methods in Project Management

Semester 2, 2023 [Normal evening] - Camperdown/Darlington, Sydney

Overview

The discovery, interpretation, and communication of meaningful patterns in project data are essential to effective decision-making. Analytics can help project managers use predictive information to make better choices. This unit of study covers how analytics could be used in project management to access feasibility, analyse project portfolio for selection and prioritization, and predict project success. Students will be developing the skills to use a range of analytics techniques with a variety of data. They will also have the opportunity to learn how to present analytics in a meaningful way for business use.

Unit details and rules

Academic unit	Project Management
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	PMGT6867
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Shahadat Uddin, shahadat.uddin@sydney.edu.au
Lecturer(s)	Haohui Lu, haohui.lu@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Questions based on the entire course materials	50%	Formal exam period	1.5 hours
Outcomes assessed:
Small test	Quiz Short questions, case study, multiple choice question (question bank)	15%	Week 06	30 minutes
Outcomes assessed:
Small test	Quiz Short questions, case study, multiple choice question (question bank)	15%	Week 12	30 minutes
Outcomes assessed:
Assignment	Group assignment Answer questions in a report based on a case study	20%	Week 13 Due date: 03 Nov 2023 at 23:59	Approximately 16 pages
Outcomes assessed:
= group assignment ?

Assessment summary

Each group needs to submit a report for answering questions from a case study

Assessment criteria

Total weight is 20%.

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:

5% for each day

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	Overview and review of basic methods	Workshop (2 hr)
Week 02	Probability	Workshop (2 hr)
Week 03	Project network variance	Workshop (2 hr)
Week 04	Machine Learning in project analytics I: Regression	Workshop (2 hr)
Week 05	Machine Learning in project analytics II: Classification	Workshop (2 hr)
Week 06	Machine Learning in project analytics III: Evaluation	Workshop (2 hr)
Week 07	WBS, Project estimations and a brief to earned value analysis	Workshop (2 hr)
Week 08	Fuzzy logic in Project Management	Workshop (2 hr)
Week 09	Decision making approaches: Payoff analysis and Utility function	Workshop (2 hr)
Week 10	The mathematics of project contracts	Workshop (2 hr)
Week 11	Project forecasting	Workshop (2 hr)
Week 12	Process capability and Control chart	Workshop (2 hr)
Week 13	Revision	Workshop (2 hr)

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

It is recommended to watch some videos on machine learning (ML) prior to the weeks covering ML and project analytics.

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. Gain a comprehensive understanding of analytics and how to apply it for project feasibility assessment.
LO2. Develop the ability to leverage predictive analytics to forecast project outcomes and inform decision-making processes.
LO3. Acquire the skills necessary to apply analytical techniques for effective project portfolio management, including selection and prioritisation.
LO4. Enhance proficiency in utilising a wide range of analytics approaches to handle diverse data types in project management scenarios.
LO5. Implement analytics skills in practical project management situations, enabling to solve real-world problems using data-driven insights.

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.

This course has a different UoS code (PMGT6867) before. However, the course title remains the same (Quantitative Methods in Project Management). New course materials based on machine learning have been included for three weeks.

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

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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