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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.


Academic unit Computer Science
Unit code COMP5310
Unit name Principles of Data Science
Session, year
Semester 1, 2023
Attendance mode Normal evening
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

INFO3406 OR OCMP5310
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


Teaching staff and contact details

Coordinator Nazanin Borhan,
Type Description Weight Due Length
Monitored exam
Written exam
Monitored Exam in Canvas with ProctorU
60% Formal exam period 2 hours
Outcomes assessed: LO3 LO4 LO5 LO9 LO10 LO2 LO8 LO1
Assignment group assignment Project Stage 1: Obtain data, clean it and load
Report submission
10% Week 04
Due date: 19 Mar 2023 at 23:59
Outcomes assessed: LO6 LO8 LO7
Assignment group assignment Project Stage 2A: Summarise and analyse the data
Report submission
10% Week 07
Due date: 06 Apr 2023 at 23:59
Outcomes assessed: LO2 LO8 LO7 LO6
Assignment group assignment Peroject Stage 2B: Develop and evaluate predictive model
Report submission
15% Week 11
Due date: 14 May 2023 at 23:59
Outcomes assessed: LO1 LO8 LO7 LO6
Presentation group assignment Project Stage 3: Online oral presentation
Oral presentation
5% Week 13
Due date: 23 May 2023 at 19:00
up to 6 minutes for the whole group
Outcomes assessed: LO6 LO8 LO10
group assignment = group assignment ?


  • Project Stage 1: Obtain data, clean it, load and summarise.
  • Project Stage 2A: Summarise and analyse the data
  • Project Stage 2B: Develop and evaluate 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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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 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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to data science and big data Lecture and tutorial (3 hr) LO3 LO4 LO8
Week 02 Data exploration with Spreadsheets Lecture and tutorial (3 hr) LO5 LO8
Week 03 Data exploration with Python Lecture and tutorial (3 hr) LO2 LO6
Week 04 Cleaning and storing data Lecture and tutorial (3 hr) LO2 LO7
Week 05 Querying and summarising data Lecture and tutorial (3 hr) LO2 LO6
Week 06 Hypothesis testing and evaluation Lecture and tutorial (3 hr) LO1 LO8
Week 07 Data mining: association rules and dimensionality reduction Lecture and tutorial (3 hr) LO1 LO7 LO9 LO10
Week 08 Data mining: clustering Lecture and tutorial (3 hr) LO1 LO7 LO9 LO10
Week 09 Machine learning: regression Lecture and tutorial (3 hr) LO1 LO7 LO9 LO10
Week 10 Machine learning: classification Lecture and tutorial (3 hr) LO1 LO7 LO9 LO10
Week 11 Unstructured data Lecture and tutorial (3 hr) LO1 LO7 LO9 LO10
Week 12 Ethics in data science Lecture and tutorial (3 hr)  
Week 13 Semester review; guest lecture Lecture and tutorial (3 hr) LO9 LO10
Weekly Study, prepare, revise, work on assessments Independent study (117 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

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
Remove small weekly assessment, change the weight of Assignments


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