Outline

Overview

Are you ready to unlock the power of data analytics? In today's world, data is essential for making informed decisions, and learning how to use it effectively is critical. This unit will show you how to harness the power of data from a range of sources to drive decision making in multiple domains. Using the R programming language, you will learn to manipulate data, perform statistical analyses, create visualisations and produce reproducible reports. With a focus on identifying and justifying appropriate data analytics techniques, by the end of this unit you'll be equipped to investigate social, scientific, and organizational problems, and then evaluate and communicate your results to diverse audiences, developing skills that are in high demand across many industries.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Year 10 mathematics or equivalent
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Diana Warren, diana.warren@sydney.edu.au
Tutor(s)	Elijah Willie, elijah.willie@sydney.edu.au

Assessment

The census date for this unit availability is 14 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Short release assignment	Project 1 report A data project based on given data.	30%	Week 04 Due date: 23 Mar 2025 at 23:59	1000 word report
Outcomes assessed:
Presentation	Project 1 presentation A data project based on given data.	10%	Week 05 Due date: 24 Mar 2025 at 18:00	Short presentation
Outcomes assessed:
Short release assignment	Project 2 report A data project based on given data, or own data.	20%	Week 07 Due date: 13 Apr 2025 at 23:59	1000 word report
Outcomes assessed:
Presentation	Project 2 presentation A data project based on given data, or own data.	10%	Week 08 Due date: 14 Apr 2025 at 18:00	Short presentation
Outcomes assessed:
Small test	Concept quizzes A short quiz based on the asynchronous material, including weekly readings.	10%	Weekly	30 min quizzes
Outcomes assessed:
Small continuous assessment	Code challenges A short R coding problem, based on the lab exercises.	10%	Weekly	30 min exercises
Outcomes assessed:
Participation	Lab participation Active engagement in the lab class, with the tutor(s) and peers.	10%	Weekly	1.5 hours each week
Outcomes assessed:
= AI allowed ?

Assessment summary

Projects

The projects are designed to give you an opportunity to showcase the skills you have learnt in your independent study and the workshops. They are individual projects, and must be submitted via Canvas. Late penalities apply. It is your responsibility to check that your project has been submitted correctly. Presentations will be held in the workshop time on the day following report submission.

Weekly Concept Quizzes

There are small, randomised quizzes that review each week’s content. Each quiz is due on Sunday at 11:59pm. There are 6 weekly quizzes, with the best 5 having 2% weighting each towards the final mark.

Weekly Code Challenges

There are small coding exercises to test content covered in the lab. Each code challenge is due on Sunday at 11:59pm. There are 6 weekly code challenges, with the best 5 having 2% weighting each towards the final mark.

Lab Participation

Active engagement and participation in labs is expected. There are 5 Labs (excluding presentation weeks - that is, weeks 1,2,3,4,6) each counting 2% towards the final mark.

Assessment criteria

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 guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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	Data Visualisation & Wrangling: data ingestion, initial data analysis, ggplot, tidyverse and wrangling to answer questions	Independent study (3 hr)
Week 01	Data Visualisation & Wrangling: application of independent study to real-world data	Workshop (1.5 hr)
Week 02	Beyond the Data Rectangle: pivoting rectangular data, joining for analysis and data quality assurance	Independent study (3 hr)
Week 02	Beyond the Data Rectangle: application of independent study to real-world data	Workshop (1.5 hr)
Week 03	Beyond the Data Rectangle: time, text and geospatial data analysis using lubridate, stringr and leaflet	Independent study (3 hr)
Week 03	Beyond the Data Rectangle: application of independent study to real-world data	Workshop (1.5 hr)
Week 04	Data Bootstrapping and Simulations: introduction to chance, samples versus population, chance error and the sampling distribution	Independent study (3 hr)
Week 04	Data Bootstrapping and Simulations: application of independent study to real-world data	Workshop (1.5 hr)
Week 05	Informed Decision Making with Data: introduction to hypothesis testing, tests for means and independence	Independent study (3 hr)
Week 05	Informed Decision Making with Data: application of independent study to real-world data	Workshop (1.5 hr)
Week 06	Informed Decision Making with Data: bootstrapping and simulation methods, confidence intervals	Independent study (3 hr)
Week 06	Informed Decision Making with Data: application of independent study to real-world data	Workshop (1.5 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.

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. Clearly explain how a diverse range of data sources and analytical methods can be effectively employed to inform decision-making across multiple domains.
LO2. Utilise the R programming language to manipulate data, perform statistical analyses, generate data visualisations, and create reproducible reports.
LO3. Identify, justify and implement appropriate data analytics techniques to investigate social, policy, scientific and organisational problems.
LO4. Evaluate and communicate the outcomes and implications of a range of statistical analyses to diverse audiences.

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 is the second time this unit has been offered.

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

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ODAT5011: Data Analytics Foundations

PG Online Session 1A, 2025 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links