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	OCMP5310
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP5310 or 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	Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Project Stage 1: Data Acquisition and Cleaning Project proposal, obtain data, clean it and load data for further analysis	10%	Week 03	Variable - Code + Report
Outcomes assessed:
Assignment	Project Stage 2: Data Summarisation and Analysis Summarise data and conduct data analysis	10%	Week 05	Variable - Code + Report
Outcomes assessed:
Presentation	Online Project Presentation Project Stage 3a: Online Oral Presentation	5%	Week 06	ca 5 min
Outcomes assessed:
Assignment	Project Stage 3: Predictive Model & Final Report Develop and evaluate predictive model; submission of project report	15%	Week 06	Variable - Code + Report
Outcomes assessed:
Supervised exam ?	Final Exam Monitored (proctored) exam in Canvas	50%	Week 08 Due date: 08 Apr 2024 at 18:30	2 hours
Outcomes assessed:
Small test	Weekly Review Quizzes Weekly Canvas quiz on concepts taught in this course.	10%	Weekly	ca 30 min per week
Outcomes assessed:

Assessment summary

Details of each assessment task appears in Canvas for the course.

The assessment contains three main components:

Knowledge (50%): This is assessed through two primary components: (1) Weekly review quizzes - these are worth 10 marks each and contribute to participation. (2) Final exam (50%) – a formal close-book exam.

Project Work (40%): This is a small data science project that will be developed incrementally through the course and which focuses on understanding the data science process from data ingestion and cleaning, over understanding data and gathering descriptive statistics, to building a (simple) predictive model. There are three main submissions, as well as a short oral presentation that is designed to train presentation skills.

Participation (10%): This is assessed based on students active participation in completing the weekly quizzes.

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	Excellent course work
Distinction	75 - 84	Very good course work
Credit	65 - 74	Good course work
Pass	50 - 64	Fair course work
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 .

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.

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:

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). There is no late submission possible for the weekly review quizzes.

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.

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

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and overview; Data Acquisition and Exploration	Independent study (6.5 hr)
Week 01	Life Session on Data Acquisition and Exploration	Tutorial (1.5 hr)
Week 02	Data Transformation and Storage	Independent study (6.5 hr)
Week 02	Life Session on Data Transformation and Storage	Tutorial (1.5 hr)
Week 03	Data Mining and Hypothesis Testing	Independent study (6.5 hr)
Week 03	Life Session on Data Mining and Hypothesis Testing	Tutorial (1.5 hr)
Week 04	Machine Learning	Independent study (6.5 hr)
Week 04	Life Session on Machine Learning and Project	Tutorial (1.5 hr)
Week 05	Analysing Unstructured Data and getting to Decisions	Independent study (6.5 hr)
Week 05	Life Session on Unstructured Data and Project	Tutorial (1.5 hr)
Week 06	Big Data and Ethics	Independent study (6.5 hr)
Week 06	Review and Final Project Presentations	Tutorial (1.5 hr)
Week 07	Study, prepare, revise, work on assessments	Independent study (16 hr)

Attendance and class requirements

Attendance at lecture and tutorials is expected.

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.

Required readings

These are specified in the weekly material.

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

Additional information

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

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

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

OCMP5310: Principles of Data Science

Semester 1a, 2024 [Online] - Online Program

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

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect