Skip to main content

We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

COMP5310: Principles of Data Science

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.

Details

Academic unit Computer Science
Unit code COMP5310
Unit name Principles of Data Science
Session, year
? 
Semester 1, 2020
Attendance mode Normal evening
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

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 and contact details

Coordinator Ali Anaissi, ali.anaissi@sydney.edu.au
Type Description Weight Due Length
Assignment Participation
10% - n/a
Outcomes assessed: LO1 LO2 LO5 LO6 LO7 LO8 LO9 LO10
Assignment group assignment Project Stage 3: Online oral presentation
5% - n/a
Outcomes assessed: LO6 LO8 LO10
Final exam Written exam
55% Formal exam period 1 hour
Outcomes assessed: LO3 LO4 LO5 LO9 LO10
Assignment group assignment Project Stage 1: Obtain data, clean it and load
10% Week 06 n/a
Outcomes assessed: LO6 LO8 LO7
Assignment group assignment Project Stage 2: Summarise and analyse the data
20% Week 12 n/a
Outcomes assessed: LO1 LO8 LO7 LO6 LO2
group assignment = group assignment ?
  • 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

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.

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>

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to data science and big data Lecture and tutorial (3 hr)  
Week 02 Data exploration with Spreadsheets Lecture and tutorial (3 hr)  
Week 03 Data exploration with Python Lecture and tutorial (3 hr)  
Week 04 Cleaning and storing data Lecture and tutorial (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 1. Product thinking and ethics: 2. Information, actionable knowledge from data, and link to effective decision making Online class (3 hr)  
Week 13 Unit of study review 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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
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.