Unit convenor and teaching staff |
Unit convenor and teaching staff
Convener, Lecturer
Yipeng Zhou
Lecturer
Greg Baker
Convener, Lecturer
Benjamin Pope
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit introduces students to the fundamental techniques and tools of data science, such as the graphical display of data, predictive models, evaluation methodologies, regression, classification and clustering. The unit provides practical experience applying these methods using industry-standard software tools to real-world data sets. Students who have completed this unit will be able to identify which data science methods are most appropriate for a real-world data set, apply these methods to the data set, and interpret the results of the analysis they have performed. Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
To pass this unit you must:
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern. For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration. For example, if the assignment is worth 8 marks (of the entire unit) and your submission is late by 19 hours (or 23 hours 59 minutes 59 seconds), 0.4 marks (5% of 8 marks) will be deducted. If your submission is late by 24 hours (or 47 hours 59 minutes 59 seconds), 0.8 marks (10% of 8 marks) will be deducted, and so on.
Weekly Practice-based task (10%)
Development of knowledge and skills requires continual practice at authentic problems in a laboratory-based setting. This unit has weekly laboratory classes and you must demonstrate your progress in developing and communicating knowledge and skills in a minimum of 8 of the 12 classes. This is a hurdle assessment meaning that failure to meet this requirement may result in a fail grade for the unit. Students are permitted up to four absences: additional absences will require approval of Special Consideration (see below)
The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through http://connect.mq.edu.au/.
Name | Weighting | Hurdle | Due |
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Weekly Tasks | 10% | Yes | 11:55 pm on Sunday of each week |
Assignment 1 | 15% | No | 11:55 pm on Sunday ending week 12 |
Critical Analysis Task | 20% | No | 11:55 pm on Sunday ending week 13 |
Assignment 2 | 15% | No | 11:55 pm on Sunday ending week 12 |
Examinations | 40% | No | Exam Period |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 0 hours
Due: 11:55 pm on Sunday of each week
Weighting: 10%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)
Development of knowledge and skills requires continual practice at authentic problems. During weekly workshops you will practice a range of tasks recording your progress on worksheets. To pass this hurdle assessment, you must be able to demonstrate your progress in developing and communicating knowledge and skills in a minimum of 8 of the 12 weekly workshops.
Assessment Type 1: Programming Task
Indicative Time on Task 2: 20 hours
Due: 11:55 pm on Sunday ending week 12
Weighting: 15%
This assessment focuses on fundamental statistical analysis, which will apply linear and logistic regression techniques on one or more data sets.
Assessment Type 1: Report
Indicative Time on Task 2: 20 hours
Due: 11:55 pm on Sunday ending week 13
Weighting: 20%
You will be given a sample notebook describing the analysis of a dataset. You will provide a critical analysis of this notebook and suggest improvements in the way that data is analysed and results are presented.
Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: 11:55 pm on Sunday ending week 12
Weighting: 15%
This assessment will consist of a number of data analysis problems that will involve writing code to analyse one or more data sets.
Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: Exam Period
Weighting: 40%
Examinations will assess your knowledge and understanding of the data analysis and machine learning methods covered in the semester.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Lecture and practical workshop will commence since Week 1
We will communicate with you via your university email and through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor via the contact email on iLearn.
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
We value student feedback to be able to continually improve the way we offer our units. Compared with the last offering, the assessment structure has been significantly revised. The number of programming based projects is reduced from 4 to 2. The reflective report and presentation task has been removed. The mid exam has been merged with the final exam. The lecture cotent has been revised to include statstical knowledge and the latest machine learning techniques.
Date | Description |
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12/02/2025 | The teaching staff team is updated |
Unit information based on version 2025.02 of the Handbook