Unit convenor and teaching staff |
Unit convenor and teaching staff
Yipeng Zhou
Guanfeng Liu
Tutor
Subhash Sagar
Tutor
Julius Lu
Tutor
Jiwei Guan
Tutor
Venus Haghighi
Tutor
Behnaz Soltani
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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
COMP2200
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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.
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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:
From 1 July 2022, Students enrolled in Session based units with written assessments will have the following university standard late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information.
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written 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. Submission time for all written assessments is set at 11:55 pm. A 1- hour grace period is 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, students need to submit an application for Special Consideration.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Weekly Submissions | 10% | Yes | every week |
Critical Analysis Task | 15% | No | week 12 |
Data Science Portfolio | 35% | No | weeks 4, 6, 8, 10 |
Examinations | 40% | No | week7 and exam week |
Assessment Type 1: Participatory task
Indicative Time on Task 2: 0 hours
Due: every week
Weighting: 10%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)
A submission of a small task based on the workshop each week. This may be a short quiz or the result of a practical task.
Assessment Type 1: Report
Indicative Time on Task 2: 15 hours
Due: week 12
Weighting: 15%
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: Portfolio
Indicative Time on Task 2: 45 hours
Due: weeks 4, 6, 8, 10
Weighting: 35%
The portfolio assessment will consist of a number of data analysis problems that you will be given through the semester. These will involve writing code to analyse one or more data sets. These will be marked individually through the semester and then as an overall portfolio at the end of semester.
Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: week7 and exam week
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
There will be one two hour online lecture each week, and one two hour workshop in the computing laboratory or online. The online lecture would be in the form of live streaming or pre-recorded lecture videos. You are expected to attend both classes as they provide complimentary learning activities each week. In practical classes you will write code and experiment with various data sets; in lectures we will discuss the methods you are learning and how the results of your analysis can be interpreted.
We will refer to the following texts during the semester:
Introduction to Data Science A Python Approach to Concepts, Techniques and Applications Igual, Laura, Seguí, Santi (electronic edition available via MQ Library)
Computational and Inferential Thinking: The Foundations of Data Science By Ani Adhikari and John DeNero (available on GitBooks)
You will be given readings from these and other sources each week.
We will make use of Python 3 for data analysis, including a range of modules such as scikit-learn, pandas, numpythat provide additional features. These can all be installed via the Anaconda Python distribution. We will discuss this environment and the installation process in the first week of classes.
We will use Jupyter Notebook as a way of developing and presenting the analysis results. This is included in the full Anaconda distribution.
Unit Schedule
The indicative list of topics is shown here, this is subject to change based on feedback from the class.
1 |
Overview of DS, Learning Python, Notebooks |
SS |
2 |
Data formats, Python input and output |
SS |
3 |
Descriptive Statistics, simple visualisation |
SS |
4 |
Causality and correlation; Visualisation |
SS |
5 |
Predictive Modelling: Linear and Logistic Regression |
SS |
6 |
Software Engineering for Data Science |
SS |
7 |
Feature Engineering; Unsupervised Learning |
SS/XZ |
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8 |
K-Nearest Neighbours Classifiers |
XZ |
9 |
Naive Bayes Classifiers |
XZ |
10 |
Artificial Neural Networks |
XZ |
11 |
Decision Tree Models |
XZ |
12 |
Advanced Topics / Guest Lecture |
Guest |
13 |
Summary |
All |
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 ask.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/
The Writing Centre 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:
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Unit information based on version 2022.03 of the Handbook