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

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 the second chance of each workshop hurdle assessment until they complete these workshops.. 

Late Assessment Submission Penalty 

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 7^th day (including weekends). After the 7^th 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. 

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.

Assessments where Late Submissions will be accepted 

Assessment Portfolios, Workshops, Reflective Report and Crtical Assessment Task– YES, Standard Late Penalty applies 

Assessment Mid Exam, Final Exam- NO, unless Special Consideration is Granted 

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 pre-recorded 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 which they have enrolled. 

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 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 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.

The indicative list of topics is shown here, this is subject to change based on feedback from the class.  

There are two changes. 

First, we will include new materials on data ethics and data privacy in week 4 and week 6. Correspondingly, assessment tasks will involve questions related to these new topics. 

Second, we do not run online workshop anymore. The mid and final exams are delivered as online quizzes on campus, and changed from open book to closed book.