Outline

Overview

Technological advances in science, business, and engineering have given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2902 is an intermediate unit in statistics and data sciences, focusing on learning advanced data analytic skills for a wide range of problems and data. In this unit, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforce your programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skills to analyse various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	A mark of 65 or greater in (DATA1X01 or ENVX1002 or BUSS1020 or ECMT1010 or MATH1062 or MATH1962 or MATH1972 or [MATH1X05 and (MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1021 or MATH1023 or MATH1115 or MATH19XX)])
Corequisites ?	None
Prohibitions ?	STAT2012 or STAT2912 or DATA2002
Assumed knowledge ?	Successful completion of a first-year or second-year unit in statistics or data science including a substantial coding component. The content from STAT2X11 will help but is not considered essential. Students who are not comfortable using the R software for statistical analysis should familiarise themselves before attempting the unit, e.g. taking OLET1632: Shark Bites and Other Data Stories
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jaslene Huan Lin, huan.lin@sydney.edu.au
Lecturer(s)	Shila Ghazanfar, shila.ghazanfar@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final exam	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Online quiz Online quiz	3%	Week 03 Due date: 24 Aug 2025 at 23:59 Closing date: 26 Aug 2025	1 hour	AI allowed
Outcomes assessed:
Written work	Assignment Individual assignment (report)	5%	Week 06 Due date: 14 Sep 2025 at 23:59 Closing date: 24 Sep 2025	2000 words (guide, not a strict limit)	AI allowed
Outcomes assessed:
Creative work	Shiny app Individual assignment (Shiny app)	10%	Week 07 Due date: 21 Sep 2025 at 23:59 Closing date: 01 Oct 2025	Documentation and app	AI allowed
Outcomes assessed:
Practical skill	Project: EDA and contract Individual exploratory data analysis and group contract submission	2%	Week 09 Due date: 05 Oct 2025 at 23:59	3 pages	AI allowed
Outcomes assessed:
Presentation	Project: presentation Oral group presentation delivered during your lab in week 12	10%	Week 12 Due date: 26 Oct 2025 at 23:59	20 slides, 20 seconds per slide	AI allowed
Outcomes assessed:
Written work	Project: report Written group report and shiny app	10%	Week 13 Due date: 09 Nov 2025 at 23:59	2 pages + 1 page appendix + shiny app	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Individual assignment (report): A statistical analysis of a data set, including exploratory data analysis, hypothesis tests.
Individual assignment (Shiny app): A Shiny app built in R to facilitate interactive visualisation and hypothesis testing with evidence of version control in GitHub and documentation.
Online quiz: This is an early feedback task, designed to give you feedback prior to the census date for this unit. It is important that you actively engage with this task so that the University can support you to be successful in this unit.
Project: The project has three components:
1. Exploratory data analysis (EDA) and group contract. The EDA is an individual task. The group contract needs to be negotiated collectively within your group. Each student submits a document that has their individual EDA and their common group contract. Submissions are due the Sunday before week 9. For Monday labs, submissions will be discussed and marked by your tutors during week 10, due to the public holiday. For all other labs, this will take place during week 9.
2. Presentation. PechaKucha style presentation (20 slides, 20 seconds per slide) done as a group.
3. Report and shiny app. Following the presentations you will compose a short report that succinctly describes your analysis and findings. An interactive shiny app will also be submitted. This is done as a group, i.e. one submission per group.
4. Final exam: Supervised exam in the formal exam period. If a second replacement exam is required, this may be conducted via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

None of the tasks are hurdle tasks, this means there is no "double pass" requirement - an overall final mark of 50 is sufficient to pass the unit.

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	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
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.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy 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. 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Module 1: categorical data	Lecture (3 hr)
Week 01	Revising R	Computer laboratory (2 hr)
Week 02	Module 1: categorical data	Lecture (3 hr)
Week 02	Module 1: categorical data	Computer laboratory (2 hr)
Week 03	Module 1: categorical data	Lecture (3 hr)
Week 03	Module 1: categorical data	Computer laboratory (2 hr)
Week 04	Module 2: data from case control study	Lecture (3 hr)
Week 04	Module 1: categorical data	Computer laboratory (2 hr)
Week 05	Module 2: data from case control study	Lecture (3 hr)
Week 05	Module 2: data from case control study	Computer laboratory (2 hr)
Week 06	Module 2: data from case control study	Lecture (3 hr)
Week 06	Module 2: data from case control study	Computer laboratory (2 hr)
Week 07	Module 3: multiple factors comparison	Lecture (3 hr)
Week 07	Module 2: data from case control study	Computer laboratory (2 hr)
Week 08	Module 3: multiple factors comparison	Lecture (3 hr)
Week 08	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 09	Module 3: multiple factors comparison	Lecture (3 hr)
Week 09	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 10	Module 4: learning and prediction	Lecture (3 hr)
Week 10	Module 3: multiple factors comparison	Computer laboratory (2 hr)
Week 11	Module 4: learning and prediction	Lecture (3 hr)
Week 11	Module 4: learning and prediction	Computer laboratory (2 hr)
Week 12	Learning and prediction	Lecture (3 hr)
Week 12	Module 4: learning and prediction	Computer laboratory (2 hr)
Week 13	Revision	Lecture (3 hr)
Week 13	Module 4: learning and prediction	Computer laboratory (2 hr)

Attendance and class requirements

Content will be delivered using a mix of pre-recorded videos and a weekly 2 hour in-person lecture. Each week you are expected to watch the pre-recorded video(s) before attending the lecture. The in-person lecture will also be recorded and available for viewing on Canvas within a few hours.

Lab attendance does not contribute directly to your final mark. However, the labs are an extremely important component of the unit and where a lot of the learning happens. Your tutor will record attendance in labs as this helps us understand engagement patterns across semester and identify students who might be at risk of failing.

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

An Introduction to Mathematical Statistics and Its Applications by Richard J. Larsen and Morris L. Marx. 5th Ed. Chapters 6-14. Chapters 1-5 are also useful background reading, though somewhat more mathematical than necessary for DATA2002. These chapters roughly corresponding to key parts of STAT2011.
R Markdown: The Definitive Guide by Yihui Xie, J.J. Allaire, Garrett Grolemund.
Quarto Tutorial: Authoring
Happy Git and GitHub for the useR by Jenny Bryan, the STAT 545 TAs and Jim Hester.
Modern Data Science with R by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. Parts I and II, Appendix B and E
R for Data Science (2e) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund.

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. formulate domain/context specific questions and deduce appropriate statistical analysis
LO2. extract and combine data from multiple data resources
LO3. construct, analyse and evaluate numerical and graphical summaries of different data types including large and/or complex data sets
LO4. have developed expertise in the use of a software version control system
LO5. identify, justify and implement appropriate parametric or non-parametric statistical tests
LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple factors
LO7. demonstrate statistical machine learning using a given classifier and design a cross-validation scheme to calculate the prediction accuracy
LO8. create a reproducible report to communicate outcomes using a programming language.

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.

The unit receives generally favourable reviews. In 2025, an early feedback quiz will be introduced to help you self-assess your readiness for the unit. In addition, weekly quizzes have been restructured as formative (non-graded) assessments to better support your learning and prepare you for the final exam. In 2024 the way the lectures are delivered has shifted to a hybrid approach using a combination of pre-recorded content to be viewed in your own time before a 2 hour in person lecture block. Previously it was three one hour in-person lectures across three different days each week. In response to feedback from the DATA2902 cohort, the individual Shiny app assessment task has now been broken out as it's own assessment item (it used to be lumped in with the individual report), given a higher weighting and due a week after the report. With a higher weighting, there will be more expected of students for this task, e.g. documentation and evidence of version control.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

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

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

Work, health and safety

Disclaimer

On this page

Useful links

DATA2902: Data Analytics: Learning from Data (Adv)

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

Work, health and safety

Disclaimer

On this page

Useful links