Outline

Overview

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

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

Teaching staff

Coordinator	Mehdi Rajabi Asadabadi, mehdi.rajabiasadabadi@sydney.edu.au
Lecturer(s)	Mehdi Rajabi Asadabadi, mehdi.rajabiasadabadi@sydney.edu.au
Tutor(s)	Upul Senanayake, upul.senanayake@sydney.edu.au
Tutor(s)	Rizwan Ahmad, rizwan.ahmad@sydney.edu.au

Type	Description	Weight	Due	Length
Participation	Participation Activities to complete during or after the class.	20%	Ongoing	Ongoing
Outcomes assessed:
Assignment	Individual Assignment Students will clean and analyse the given data set	20%	Week 07 Due date: 10 Apr 2022 at 20:00	1500 words
Outcomes assessed:
Online task	Online Quiz Students will do an online quiz based on the content covered in week1-week5	15%	Week 08 Due date: 15 Apr 2022 at 20:00	45 minutes
Outcomes assessed:
Assignment	Group Assignment Students will work in groups on a given case study	20%	Week 11 Due date: 15 May 2022 at 18:00	2000 words
Outcomes assessed:
Assignment	Group Presentation Students group will be presenting their group assignments to class	10%	Week 12 Due date: 20 May 2022 at 18:00	5 Minutes
Outcomes assessed:
Online task	Online Quiz Students will do an online quiz	15%	Week 13 Due date: 27 May 2022 at 20:00	45 minutes
Outcomes assessed:
= group assignment ?

Assessment summary

Weekly assessments: Studensts will be asked to submit solutions to questions asked during the workshops

Class Participation: Students will be marked on their engaement in class both with peers and with teacher

Individual Assignment: Students will be doing some problem solving related to a given project.

Group Assignment : Students will be working in groups of 4-5 to use analytical skills to help management gain insights on the given case study.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
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.

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:

Late submissions will incur a penalty of 5% per day for up to 7 days starting directly after the cut-off time. This means 5% will be deducted even if submitted on the due day but after the cut-off time. After one week, assessments will no longer be marked and receive a 0 mark.

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

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

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. 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
Learning outcomes

Responding to student feedback

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

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

Disclaimer

On this page

Useful links

ENGG2851: Project Analytics

Semester 1, 2022 [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

Disclaimer

On this page

Useful links