Outline

Overview

The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualisation of data- with the goal of enabling discovery of information and knowledge to guide effective decision making and to gain new insights from large data sets. To this end, this unit of study provides a broad introduction to data management, analysis, modelling and visualisation using the Python programming language. Development of custom software using the powerful, general-purpose Python scripting language; Data collection, cleaning, pre-processing, and storage using various databases; Exploratory data analysis to understand and profile complex data sets; Mining unlabelled data to identify relationships, patterns, and trends; Machine learning from labelled data to predict into the future; Communicate findings to varied audiences, including effective data visualisations. Core data science content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.

Unit details and rules

Unit code	COMP5310
Academic unit	Computer Science
Credit points	6
Prohibitions ?	INFO3406
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Good understanding of relational data model and database technologies as covered in ISYS2120 or COMP9120 (or equivalent UoS from different institutions).
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Ali Anaissi, ali.anaissi@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Written exam Type D	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Participation	10%	Ongoing	n/a
Outcomes assessed:
Assignment	Project Stage 1: Obtain data, clean it and load	10%	Week 06	n/a
Outcomes assessed:
Assignment	Project Stage 2: Summarise and analyse the data	15%	Week 12	n/a
Outcomes assessed:
Assignment	Project Stage 3: Online oral presentation	5%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Participation: Complete and submit lab exercises.
Project Stage 1: Obtain data, clean it, load and summarise.
Project Stage 2: Analyse the data, develop and test a predictive model.
Project Stage 3: Presentation of results.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

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. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. 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.

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 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:

In accordance with university policy, these penalties apply when written work is submitted after 11:59pm on the due date. <ul> <li>Deduction of 5% of the maximum mark for each calendar date after the due date.</li><li> After ten calendar days late, a mark of zero will be awarded.</li></ul>

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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
Week 01	Introduction to data science and big data	Online class (3 hr)
Week 02	Data exploration with Spreadsheets	Online class (3 hr)
Week 03	Data exploration with Python	Online class (3 hr)
Week 04	Cleaning and storing data	Online class (3 hr)
Week 05	Querying and summarising data	Online class (3 hr)
Week 06	Hypothesis testing and evaluation	Online class (3 hr)
Week 07	Data mining: association rules and dimensionality reduction	Online class (3 hr)
Week 08	Data mining: clustering	Online class (3 hr)
Week 09	Machine learning: regression	Online class (3 hr)
Week 10	Machine learning: classification	Online class (3 hr)
Week 11	Unstructured data	Online class (3 hr)
Week 12	Unit of study review	Online class (3 hr)
Week 13	Product Thinking and Ethics	Online class (3 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. select statistical techniques appropriate for evaluation of a predictive model that is based on data analysis, and justify this choice
LO2. select statistical techniques appropriate for summarisation and analysis of a data set, and justify this choice
LO3. apply concepts and terms from social science to describe and analyse the role of a data analysis task in its organisational context
LO4. understand the role of data science in decision-making
LO5. understand the technical issues that are present in the stages of a data analysis task and the properties of different technologies and tools that can be used to deal with the issues
LO6. process large data sets using appropriate technologies
LO7. carry out (in guided stages) the whole design and implementation cycle for creating a pipeline to analyse a large heterogenous dataset
LO8. seek details of how to use a method or tool in the data analytic process
LO9. communicate the results produced by an analysis pipeline, in oral and written form, including meaningful diagrams
LO10. communicate the process used to analyse a large data set, and justify the methods used.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

COMP5310: Principles of Data Science

Semester 2, 2021 [Normal evening] - Remote

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect