Master of Data Science
DASC18MTV1
• GPA of 4.50 (out of 7.00)
| Minimum number of credit points for the degree | 64 |
| Minimum number of credit points at 600 level or above | 16 |
| Minimum number of credit points at 800 level or above | 48 |
| Completion of other specific minimum requirements as set out below |
In order to graduate students must ensure that they have satisfied all of the general requirements of the award.
Specific minimum requirements:
Credit points
600 level
800 level
TOTAL CREDIT POINTS REQUIRED FOR THIS PROGRAM
| AQF Level | Level 9 Masters by Coursework Degree |
| CRICOS Code | 080284J |
| Overview and Aims of the Program | Data has the potential to transform the way business, government, science and healthcare is carried out - but conventional manual techniques aren’t capable of handling the deluge of data that modern systems generate. Data science which studies specialised computational and statistical techniques to analyse such “big data”, is considered one of the key skills of the 21st century and there is a skills shortage in this field. This program will provide students with the range of technical skills necessary to work as competent data scientists. In particular, this interdisciplinary program will help students: • apply technical skills in areas such as business and scientific applications, forensic investigation and research • develop skills such as data management on and off the cloud, machine learning, statistical modelling, and embrace big data technologies that are not currently included in many Australian university degrees • master the aspects of computer science, mathematics and statistics required to apply data science to real world issues, and • choose electives to expand their knowledge of data science via advance units in the area they want to specialise in, such as data security management or modern computational statistical methods. |
| Graduate Capabilities | The Graduate Capabilities Framework articulates the fundamentals that underpin all of Macquarie’s academic programs. It expresses these as follows: Interpersonal or social capabilities |
| Program Learning Outcomes | By the end of this program it is anticipated you should be able to: 1. apply machine learning and data mining techniques for analysing both small and large quantities of data (K) 2. skillfully organise data in a way that facilitates prediction of future trends (K) 3. demonstrate competence in employing specialised data management strategies pertinent to big data technologies on and off the cloud (K) 4. automatically analyse natural language text (in a language such as English) by employing relevant computational techniques (K) 5. explain the theoretical foundations of machine learning, natural language processing and data mining (K, P) 6. proficiently employ pertinent mathematical/statistical modelling techniques (K, P) 7. effectively use research and analysis skills necessary to carry out independent, real world data analysis using a variety of different techniques (P, J) 8. effectively integrate research and analysis skills based on a variety of specialized techniques such as statistical modelling, machine learning and data mining, and exploit them to solve real world problems (T, P) 9. describe and explain the social, moral and legal issues relevant to machine learning, data mining, natural language processing and big data technologies (E) 10. Effectively use techniques for communicating complex ideas and results in simple, effective ways (C). |
| Learning and Teaching Methods | The master of Data Science program provides students the opportunity to gain both theoretical as well as practical knowledge in data science. The core content of the program focuses on knowledge and skills in relevant fields including Machine learning, Data Mining, Computational Statistics and Text Processing, with a Capstone Project unit on Data Science. In the Practical Sessions students will be able to employ the theoretical knowledge to solve realistic problems. Tutorials/Workshops will help reinforce the theoretical concepts students have been exposed to during lectures. Although majority of information will be delivered by face-to-face lectures, audio/visual recordings of the lectures will be made available to who may miss lectures, or want to re-visit the material. Data Science being a rapidly evolving field, all Learning and Teaching material will be regularly updated. Furthermore, in different units students will come across different teaching practices. For instance, in the Capstone Unit, a small group of students with complimentary skill-sets may be assigned a mentor from an appropriate industry, and the group will complete a project relevant to then industry sponsor to their satisfaction. Master of Data Science students will learn to effectively communicate concepts relevant to their study in various forms including oral presentations, written assignments and project reports. The tasks involved will be a mix of individual as well as group tasks. One of the core units will expose students to various ethical issues that confront data science. |
| Assessment | Different units use different assessment practices, however all units have at least three different types of assessment. The majority of assessments are based on the submission of individual coursework in the form of program development and implementation, problem solving, and assignment reports. Most units have end of the semester examinations where student’s overall skills and knowledge as relevant to the unit get assessed. Assessment types vary across units. While some units incorporate continuing small assessment tasks, others involve substantive reports at appropriate intervals. Clear standards and criteria for all for different assessment tasks are provided in the unit guides. In most units the assessment tasks build up incrementally, and the summative feedback they receive in one functions as formative feedback for the next. Since most units in this program involve practical problem solving, students learn through doing. |
| Recognition of Prior Learning | Macquarie University may recognise prior formal, informal and non-formal learning for the purpose of granting credit towards, or admission into, a program. The recognition of these forms of learning is enabled by the University’s Recognition of Prior Learning (RPL) Policy (see www.mq.edu.au/policy) and its associated Procedures and Guidelines. The RPL pages contain information on how to apply, links to registers, and the approval processes for recognising prior learning for entry or credit. Domestic Students International Students |
| Support for Learning | Macquarie University aspires to be an inclusive and supportive community of learners where all students are given the opportunity to meet their academic and personal goals. The University offers a comprehensive range of free and accessible student support services which include academic advice, counselling and psychological services, advocacy services and welfare advice, careers and employment, disability services and academic skills workshops amongst others. There is also a bulk billing medical service located on campus. |
| Program Standards and Quality | The program is subject to an ongoing comprehensive process of quality review in accordance with a pre-determined schedule that complies with the Higher Education Standards Framework. The review is overseen by Macquarie University's peak academic governance body, the Academic Senate and takes into account feedback received from students, staff and external stakeholders. |
| Graduate Destinations and Employability | Data Science is in demand globally. Students of the program will be qualified to undertake international Data Science projects and related jobs as they will learn subjects which are internationally relevant, such as Data Mining, Big Data and Natural Language Processing. The current demand in the Australia and the US for Data Scientist outstrips supply by a ratio of 6 jobs to every 1 qualified resource. This skills and training obtained from this program are likely to keep students graduating from it highly employable in the foreseeable future. |
| Assessment Regulations | This program is subject to Macquarie University regulations, including but not limited to those specified in the Assessment Policy, Academic Honesty Policy, the Final Examination Policy and relevant University Rules. For all approved University policies, procedures, guidelines and schedules visit www.mq.edu.au/policy. |
| Accreditation | This is an Australian Qualifications Framework (AQF) accredited qualification. Accreditation will be sought in due course from the Australian Computer Society (http://www.acs.org.au/). |
Inherent requirements are the essential components of a course or program necessary for a student to successfully achieve the core learning outcomes of a course or program. Students must meet the inherent requirements to complete their Macquarie University course or program.
Inherent requirements for Macquarie University programs fall under the following categories:
Physical: The physical inherent requirement is to have the physical capabilities to safely and effectively perform the activities necessary to undertake the learning activities and achieve the learning outcomes of an award.
Cognition: The inherent requirement for cognition is possessing the intellectual, conceptual, integrative and quantitative capabilities to undertake the learning activities and achieve the learning outcomes of an award.
Communication: The inherent requirement for communication is the capacity to communicate information, thoughts and ideas through a variety of mediums and with a range of audiences.
Behavioural: The behavioural inherent requirement is the capacity to sustain appropriate behaviour over the duration of units of study to engage in activities necessary to undertake the learning activities and achieve the learning outcomes of an award.
For more information see https://students.mq.edu.au/study/my-study-program/inherent-requirements