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Unit of study_

PMGT2851: Project Analytics

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Project data analysis is required to inform decision making. This becomes increasingly significant as project becomes larger and more complex. This subject introduces students to a variety of fundamental analytical techniques used in project management including consideration of their assumptions, limitations, how and when they should be applied.

Unit details and rules

Unit code PMGT2851
Academic unit Project Management
Credit points 6
(PMGT1860 or ENGG1860) or (PMGT1850 or ENGG1850)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Jin XUE,
Lecturer(s) Jin XUE,
Type Description Weight Due Length
Participation Participation
Activities to complete during or after the class.
20% Ongoing Ongoing
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Online task Online Quiz
Students will do an online quiz based on the content covered in week1-week5
15% Week 06 45 minutes
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment Individual Assignment
Students will clean and analyse the given data set
27.5% Week 07
Due date: 14 Apr 2024 at 23:59
1500 words
Outcomes assessed: LO1 LO3 LO4 LO5
Assignment group assignment Group Assignment
Students will work in groups on a given case study
15% Week 12 2000 words
Outcomes assessed: LO2 LO3 LO4 LO5
Online task Online quiz
Students will do an online quiz
15% Week 12 45 minutes
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment group assignment Group Presentation
Students group will be presenting their group assignments to class
7.5% Week 13 5 Minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?

Assessment summary

This unit of study consists of 5 tasks that contribute to students' final grades.

Participation (worth: 20% of grade): Active engagement during class activities will earn students up to 20% of their final grade. By participating and completing the activities, students can secure a significant portion of their overall mark.

Individual assignment (worth: 27.5% of grade): In this assignment, students will analyze a provided dataset and prepare a report, using the tools and techniques learned in the course. Demonstrating proficiency can earn students up to 27.5% of their final grade.

Quiz 1 (worth 15% of grade): To enrich the learning experience, an online quiz will be held in week 6. This quiz serves as an opportunity for students to receive valuable feedback on their studies throughout Week 1-5.

Group assignment (worth: 22.5% of grade): Students will collaborate in groups of 4-5 to apply their analytical skills in solving a case study project (15%). They will then present their solutions (7.5%) to help management make informed decisions.

Quiz 2 (worth 15% of grade): Quiz 2 serves as the final assessment in the course.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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:

For every calendar day up to and including ten calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to late work. The penalty will be calculated by first marking the work, and then subtracting 5% of the maximum awardable mark for each calendar day after the due date. Example: Consider an assignment's maximum awardable mark is 10; the assignment is submitted 2 days late; and the assignment is marked as 7/10. After applying the penalty, marks will be: 7 - (0.5 x 2) = 6/10. For work submitted more than ten calendar days after the due date 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.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Multiple weeks pre-class and post-class independent learning Independent study (130 hr) LO1 LO2 LO3 LO4 LO5
Week 01 Overview and introduction Workshop (2 hr) LO1
Week 02 Data Basics Workshop (2 hr) LO1 LO3 LO4
Week 03 Basic analytical tools Workshop (2 hr) LO2 LO3 LO4 LO5
Week 04 Descriptive Analytics Workshop (2 hr) LO2 LO4 LO5 LO6 LO7
Week 05 Diagnostic Analytics Workshop (2 hr) LO2 LO3 LO4 LO5 LO6 LO7
Week 06 Quiz Workshop (2 hr) LO1 LO2 LO3 LO4
Week 07 Data Visualisation and Dash Boarding Workshop (2 hr) LO3 LO4 LO5
Week 08 Predictive Analytics Workshop (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Week 09 Prescriptive Analytics Workshop (2 hr) LO2 LO3 LO4 LO5
Week 10 Automated Analytical Tools Workshop (2 hr) LO3 LO4 LO5
Week 11 Student Presentations Workshop (2 hr) LO3 LO4 LO5
Week 12 Student Presentations Workshop (2 hr) LO3 LO4 LO5
Week 13 Quiz Workshop (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

Study commitment: 

This unit of study is comprised of online learning and workshops. The workshop participation forms a significant component of the course and will demonstrate specific techniques discussed at a theoretical level in online learning. Workshop participants will include case study reviews, discussions, and some problem-solving exercises carried out individually or in groups. These sessions will also introduce students to the team-based nature of projects and provide opportunities for small group problem solving and discussion, based around case studies and model problems arising from realistic technical and business scenarios.

Attendance requirement

It is a requirement of this course that you must attend more than 75% of the workshops. This means students who fail to attend more than 3 workshops without prior approval will fail the course.

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

There are no prescribed readings for the course. Relevant readings will be embedded in weekly modules.

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. Explain the purpose of measurement in different project contexts (hard/soft, large/small, complex/not, different domains, etc.)
  • LO2. Explain the uses and limitations of measurement in different project contexts
  • LO3. Explore a range of analytical tools and techniques for monitoring and control that are appropriate to different project contexts
  • LO4. Select and apply analytic tools and techniques to analyse situations, financial and organisational data, and trends
  • LO5. Apply analytical tools and techniques to specific project contexts
  • LO6. Monitor progress and make any necessary adjustments to parameters
  • LO7. Review and evaluate project performance at handover

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

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

Changes made in learning activities and assessments to suit 13 week semester


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.