Requirements to Pass this Unit

To pass this unit you must:

• Achieve a total mark equal to or greater than 50%, and 
• Participate in, and undertake all hurdle activities for, a minimum of 8 of the 12 weekly workshops

Hurdle Assessments:

Assessment 1-Practical-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 workshop practicals. This is a hurdle assessment meaning that failure to meet this requirement may result in a fail grade for the unit. It is allowed to take a second chance at each workshop hurdle assessment until they complete these workshops. Additional absences will require approval of Special Consideration (see below)

Late submission

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. The late submission rule was changed to align with the new Faculty policy.

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.

Assessments where Late Submissions will be accepted 

• Assessment Portfolios – YES, Standard Late Penalty applies
• Assessment Workshops – YES, Standard Late Penalty applies
• Assessment Reflective Report – YES, Standard Late Penalty applies
• Assessment Critical Assessment Task – YES, Standard Late Penalty applies
• Assessment Mid Exam – NO, unless Special Consideration is Granted
• Assessment Final Exam – NO, unless Special Consideration is Granted

Special Consideration

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.

Written Assessments: If you experience circumstances or events that affect your ability to complete the written assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.

Weekly practice-based tasks: To pass the unit you need to demonstrate ongoing development of skills and application of knowledge in 8 out of 12 of the weekly practical classes. If you miss a weekly practical class due to a serious, unavoidable, and significant disruption, contact your convenor ASAP as you may be able to attend another class that week. If it is not possible to attend another class, you should still contact your convenor for access to class material to review in your own time.

Classes

There will be one two-hour on-campus lecture each week, and one two-hour workshop in the computing laboratory. The on-campus lecture would be in the form of live streaming or prerecorded lecture videos as well. 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.

Week 1 Classes In week 1, there is a two-hour lecture. Workshops will commence in week 1 as well. Every student should attend the workshop session in which they have enrolled. For details of days, times, and rooms consult the timetable system.

Methods of Communication

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.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during the semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.

Textbooks

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.

Technology Used and Required

We will make use of Python 3 for data analysis, including a range of modules such as scikitlearn, pandas, and numpy that 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.

• Week 1: Overview of DS, Learning Python, Notebooks
• Week 2: Data formats, Python input and output
• Week 3: Descriptive Statistics, Simple visualisation
• Week 4: Causality and correlation; Visualisation, Data Ethics
• Week 5: Predictive Modelling: Linear and Logistic Regression
• Week 6: Software Engineering for Data Science and Data Privacy
• Week 7: Feature sets and spaces; Unsupervised learning
• Week 8: Supervised Learning: K-Nearest Neighbours
• Week 9: Naive Bayes Classifiers
• Week 10: Artificial Neural Networks
• Week 11: Learning Decision Trees
• Week 12: Summary
• Week 13: Data Science Guest Lecture

Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from the teaching staff. As such, no change to the delivery of the unit is planned, however, we will continue to strive to improve the level of support and the level of student engagement.