Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor/Lecturer
Xuyun Zhang
Contact via Email
Room 287 BD Building, 4 Research Park Drive, Macquarie Park, NSW 2109
Lecturer
Sonit Singh
Contact via Email
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
(COMP1000 or COMP115 or COMP1010 or COMP125) and (STAT1170 or STAT170 or STAT1371 or STAT171 or STAT1250 or STAT150)
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
COMP6200
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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:
No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total. No submission will be accepted after solutions have been posted.
If you receive special consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.
Name | Weighting | Hurdle | Due |
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Data Science Portfolio | 20% | No | Weeks 4, 6 & 8 for feedback. Week 10 final. |
Final Exam | 40% | No | Final Exam Period |
Weekly Submissions | 10% | Yes | Weekly |
Data Science Project | 30% | No | Week 7, Week 13 |
Assessment Type 1: Portfolio
Indicative Time on Task 2: 30 hours
Due: Weeks 4, 6 & 8 for feedback. Week 10 final.
Weighting: 20%
The portfolio assessment will consist of three small data analysis problems that you will be given through the semester. These will involve writing code to analyse one or more data sets. You will show the versions in the workshops for feedback and then submit a final version towards the end of semester.
Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: Final Exam Period
Weighting: 40%
The exam will assess your knowledge and understanding of the data analysis and machine learning methods covered in the semester.
Assessment Type 1: Participatory task
Indicative Time on Task 2: 0 hours
Due: Weekly
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: 40 hours
Due: Week 7, Week 13
Weighting: 30%
In groups of 3-4, students will be given or will find one or more datasets and are asked to develop an analysis of this data and present a report. This project should include using more than one dataset, cleaning and analysing the data, training at least two different predictive models and using the model to make some conclusions. The report should be reproducible, all methods not only documented but available as an executable archive along with the data.
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.
A major part of the assessment in this unit is based on a project that you will complete in groups. This will allow you to explore the techniques you are learning in class in a real-world data analysis exercise.
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
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Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
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Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
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