Outline

Overview

This unit provides conceptual and practical introduction covering data wrangling and database management. Students will gain a broad understanding of the capabilities for data wrangling and database management, on which effective data analysis depends. It will provide understanding of the implications for their analysis work, of the data management capabilities, and the language and ideas to communicate with the data engineers. The unit covers topics such as 1) data storage architectures 2) relational and other data models 3) data integrity 4) data privacy and security 5) data cleaning and pre-processing, and 6) data consolidation/integration.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic computer literacy
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au

Assessment

The census date for this unit availability is 22 August 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Data analysis	Data Quality Management and Databases Improving and maintaining the quality of data maintained in an organisation	10%	Week 02 Due date: 14 Aug 2025 at 23:59 Closing date: 04 Sep 2025	1.5 Weeks	AI allowed
Outcomes assessed:
Data analysis	Data Handling and Integration Focusing on data matching and linking processes	10%	Week 04 Due date: 24 Aug 2025 at 23:59 Closing date: 14 Sep 2025	1.5 Weeks	AI allowed
Outcomes assessed:
Written work	Survey – Data Handling and Integration Survey on data handling and integration for selected industry	18%	Week 06 Due date: 14 Sep 2025 at 23:59 Closing date: 05 Oct 2025	3 Weeks	AI allowed
Outcomes assessed:
Written exam ?	Final Exam Proctored exam on Canvas	50%	Week 08 Due date: 24 Sep 2025 at 18:30	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Weekly Worksheet Submissions Report Submission. No late submissions.	12%	Weekly	Weekly based Assessment.	AI allowed
Outcomes assessed:

Assessment summary

Assignments: Demonstrating the knowledge and skills from a given problem description.

Final exam: The final exam covers all aspects of the unit.

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 of a very high standard, a credit of a good standard, and a pass of 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. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45, regardless of their average.

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.

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 penalties are applied as per University policy (5% of the maximum mark per day, up to a maximum of 10 days, after which a mark of zero is awarded). No late submission is permitted for the weekly tasks since it is part of the continuous assessment from the weekly synchronous sessions.

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	Overview of Data Management Concerns and Capabilities	Independent study (8 hr)
Week 01	Overview of Data Management Concerns and Capabilities	Tutorial (1.5 hr)
Week 02	The Relational Data Model	Independent study (8 hr)
Week 02	The Relational Data Model	Tutorial (1.5 hr)
Week 03	Diversity of Data Types	Independent study (8 hr)
Week 03	Diversity of Data Types	Tutorial (1.5 hr)
Week 04	Data Integrity, Privacy and Security	Independent study (8 hr)
Week 04	Data Integrity, Privacy and Security	Tutorial (1.5 hr)
Week 05	Data Cleaning and Pre-Processing	Independent study (8 hr)
Week 05	Data Cleaning and Pre-Processing	Tutorial (1.5 hr)
Week 06	Data Consolidation/Integration	Independent study (8 hr)
Week 06	Data Consolidation/Integration	Tutorial (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. Explain the primary concerns and capabilities of data management and compare various architectures for data storage and processing.
LO2. Apply conceptual database modelling techniques to design domain-specific relational databases and understand the connection between relational and data frame models.
LO3. Use SQL to manipulate relational data, including performing data aggregation, filtering, joining tables, and grouping.
LO4. Implement efficient query processing and optimisation techniques using a formal query language, i.e., relational algebra.
LO5. Analyse non-tabular data types such as document, graph, spatial, and timeseries and understand their unique characteristics and use cases.
LO6. Explain the importance of data integrity, understand the challenges involved, and apply mechanisms to define and enforce constraints to maintain data integrity.
LO7. Evaluate and apply access control, anonymisation, and encryption mechanisms to secure data and ensure privacy.
LO8. Consolidate data from different sources and apply data cleaning and pre-processing techniques to create a unified and coherent dataset in preparation for analysis.

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.

No changes have been made since this unit was last 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)

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

ODAT5013: Data Wrangling and Databases

PG Online Session 2A, 2025 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links