Submission of assignments will be for the most part via iLearn; presentations associated with assignments will be given and assessed during class time.

For policy on late assignments, see Policies and Procedures.

CLASSES

Each week of COMP777 has a three-hour session which is a mix of lecture (typically for the first two hours), tutorial and practical session. For details of days, times and rooms, consult the University timetables webpage (http://www.timetables.mq.edu.au).

REQUIRED AND RECOMMENDED TEXTS AND/OR MATERIALS

There is no set text for the unit.  We will be providing pointers to reading material over the course of the unit.

The unit has some parallels with the freely available Software Carpentry course.  We'll be using those resources as supplementary ones for the unit.

UNIT WEBPAGE AND TECHNOLOGY USED AND REQUIRED

Web Home Page

COMP777 will make extensive use of the iLearn course management system, including for delivery of class materials, discussion boards, submission of work and access to marks and feedback. Students should check the iLearn site (https://ilearn.mq.edu.au) regularly for unit updates.

Questions and general queries regarding the content of this unit, its lectures or mixed classes, or its assignments should be posted to the discussion boards on the COMP777 iLearn site. In particular, any questions which are of interest to all students in this unit should be posted to one of these discussion boards, so that everyone can benefit from the answers. Questions of a private nature should be directed to the unit teaching staff.

Technology Used and Required

The practical work in this unit involves programming in the Python language (http://www.python.org/) which is widely used for the sorts of scripting purposes covered in this unit. Python can be downloaded free of charge for a range of operating systems from the Python website.  

Note that as this is a master's unit, there will be some self-directed learning.  We do not expect that you will know Python before the unit starts, but will pick up the necessary elements in the first few weeks of the unit; we will give pointers to resources for learning Python, and will include snippets of Python in lecture notes where relevant to computational experiments.  We will generally (but not always) use Jupyter Notebooks for Python examples, and will use Google Colab as the environment for running them.  (Google Colab is a free environment that can used for some sorts of data analysis relevant to practical assignments and the major project.)

The unit will also use various other tools for e.g. data gathering and annotation.  Much of the work that involves cloud computing will be carried out in the Amazon AWS framework; students can get access to a free, twelve-month educational licence for its use.

The focus of this unit is understanding the notions of open science and reproducible research.  Much work in both academia and industry is driven by the free availability of papers, code and data that allow the replication and extension of existing work.  In this unit, your major project will involve getting access to some of these resources, reproducing some existing work with the original data, and then investigating whether e.g. the replication works with new data.  To engage fully with these freely available resources, competence with a range of techniques and tools is necessary.

Below is a tentative schedule.  The weekly topics are intended to cover useful techniques and tools for carrying out your data-oriented project, and may change depending upon chosen student projects, etc.

• The set of tools used this year (e.g. Amazon AWS) will be largely different from previous offerings. 
• The focus of the project on (research) replication will have a different emphasis.

COMP777 will be graded according to the following general descriptions of the letter grades as specified by Macquarie University.  In the course of the unit, these grade descriptions will be discussed with respect to example projects.

High Distinction (HD, 85-100): provides consistent evidence of deep and critical understanding in relation to the learning outcomes. There is substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critical evaluation of problems, their solutions and their implications; creativity in application as appropriate to the discipline.

In the context of this unit, the project has a good design, and has used some data that is interesting or non-obvious, or has required some effort to obtain or use.  It involves a good analysis of the data, and fairly extensively draws on the techniques and tools presented in the unit and possibly on others discovered independently by the student.  The project is described in a report and a presentation that are well-structured and essentially free from errors; these would be of a standard that could be presented at a conference with little or no polishing.

Distinction (D, 75-84): provides evidence of integration and evaluation of critical ideas, principles and theories, distinctive insight and ability in applying relevant skills and concepts in relation to learning outcomes. There is demonstration of frequent originality in defining and analysing issues or problems and providing solutions; and the use of means of communication appropriate to the discipline and the audience.

In the context of this unit, the project has a good design, and has used some data that is interesting or non-obvious, or has required some effort to obtain or use.  It involves a good analysis of the data, and fairly extensively draws on the techniques and tools presented in the unit.  The project is described in a report and a presentation that are well-structured and mostly free from errors; these would be of a standard that could be presented at a conference with some polishing.

Credit (Cr, 65-74): provides evidence of learning that goes beyond replication of content knowledge or skills relevant to the learning outcomes. There is demonstration of substantial understanding of fundamental concepts in the field of study and the ability to apply these concepts in a variety of contexts; convincing argumentation with appropriate coherent justification; communication of ideas fluently and clearly in terms of the conventions of the discipline.

In the context of this unit, the project has a sound design, and demonstrates some thought in the choice of data.  It involves a good analysis of the data, and uses a reasonable number of the techniques and tools presented in the unit.  The project is described in a report and a presentation that are well-structured and mostly free from errors.

Pass (P, 50-64): provides sufficient evidence of the achievement of learning outcomes. There is demonstration of understanding and application of fundamental concepts of the field of study; routine argumentation with acceptable justification; communication of information and ideas adequately in terms of the conventions of the discipline. The learning attainment is considered satisfactory or adequate or competent or capable in relation to the specified outcomes.

In the context of this unit, the project has a satisfactory design and uses some easily accessible data.  It involves a successful, or nearly successful, analysis of data, and shows some familiarity with tools or techniques presented in the unit.  The project is described in a satisfactory report and presentation.

Fail (F, 0-49): does not provide evidence of attainment of learning outcomes. There is missing or partial or superficial or faulty understanding and application of the fundamental concepts in the field of study; missing, undeveloped, inappropriate or confusing argumentation; incomplete, confusing or lacking communication of ideas in ways that give little attention to the conventions of the discipline.