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Unit outline_

ODAT5013: Data Wrangling and Databases

Semester 2a, 2024 [Online] - Online Program

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
The census date for this unit availability is 16 August 2024
Type Description Weight Due Length
Assignment Data Quality Management and Databases
Improving and maintaining the quality of data maintained in an organisation
10% Week 02
Due date: 11 Aug 2024 at 23:59
1.5 Weeks
Outcomes assessed: LO1 LO2 LO3 LO7
Assignment Data Handling and Integration
Focusing on data matching and linking processes
10% Week 04
Due date: 22 Aug 2024 at 23:59
1.5 Weeks
Outcomes assessed: LO4 LO5 LO8
Assignment Survey – Data Handling and Integration
Survey on data handling and integration for selected industry
18% Week 06
Due date: 08 Sep 2024 at 23:59
3 Weeks
Outcomes assessed: LO1 LO4 LO5 LO6 LO7 LO8
Supervised exam
? 
Final Exam
Proctored exam on Canvas
50% Week 08
Due date: 18 Sep 2024 at 18:30
2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Small continuous assessment Weekly Worksheet Submissions
Report Submission. No late submissions.
12% Weekly Weekly based Assessment.
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3 LO2

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.

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:

A penalty of 5% per day late, e.g., a good assignment that typically gets 9/10 and is 2 days late loses 10% of the total 10 marks, i.e. new mark = 8/10. An average assignment that typically gets 5/10 and is 5 days late loses 25% of the total 10 marks, i.e. new mark = 2.5/10. Assignments more than 5 days late get 0. 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 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.

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
Week 01 Overview of Data Management Concerns and Capabilities Independent study (8 hr) LO1 LO5 LO7
Overview of Data Management Concerns and Capabilities Tutorial (1.5 hr) LO1 LO5 LO7
Week 02 The Relational Data Model Independent study (8 hr) LO2 LO3 LO5 LO7
The Relational Data Model Tutorial (1.5 hr) LO2 LO3 LO5 LO7
Week 03 Diversity of Data Types Independent study (8 hr) LO1 LO4 LO5 LO7
Diversity of Data Types Tutorial (1.5 hr) LO1 LO4 LO5 LO7
Week 04 Data Integrity, Privacy and Security Independent study (8 hr) LO1 LO5 LO6 LO7 LO8
Data Integrity, Privacy and Security Tutorial (1.5 hr) LO1 LO5 LO6 LO7 LO8
Week 05 Data Cleaning and Pre-Processing Independent study (8 hr) LO1 LO5 LO7
Data Cleaning and Pre-Processing Tutorial (1.5 hr) LO1 LO5 LO7
Week 06 Data Consolidation/Integration Independent study (8 hr) LO5 LO6 LO7 LO8
Data Consolidation/Integration Tutorial (1.5 hr) LO5 LO6 LO7 LO8

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 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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

ODAT5013 - Data Wrangling and Databases is the first time the unit outline has been created.

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.