Data Science

This major must be completed as part of an award. The general requirements for the award must be satisfied in order to graduate.

Requirements for the Major:

Completion of a minimum of 36 credit points including the following prescribed units:

Credit points

100 level

Required

3

Introduction to Computer Programming (3)

COMP115

Required

3

Fundamentals of Computer Science (3)

COMP125

Required

3

Introduction to Database Design and Management (3)

ISYS114

Required

either
or

Introductory Statistics (3)

STAT170

3

Statistical Data Analysis (3)

STAT171

200 level

Required

3

Algorithms and Data Structures (3)

COMP225

Required

3

Data Science (3)

COMP257

Required

3

Database Systems (3)

ISYS224

Required

either
or

Applied Statistics (3)

STAT270

3

Statistics I (3)

STAT271

300 level

Required

3

Big Data (3)

C

COMP336

Required

3

Computing Industry Project (3)

P

ISYS358

Required

3

Graphics, Multivariate Methods and Data Mining (3)

STAT302

Required

3cp from

Programming Languages (3)

COMP332

Document Processing and the Semantic Web (3)

COMP348

3

Linear Models (3)

STAT375

TOTAL CREDIT POINTS REQUIRED TO SATISFY THIS MAJOR

36

Note:

This major cannot be doubled with Web Design and Development, Information Systems and Business Analysis, Software Technology or Cyber Security.

Units marked with a C are Capstone units.

Units marked with a P are PACE units.

Overview and Aims of the Program	This Major is designed for students who wish to pursue a career in Data Science (DS). This emerging field has the potential to transform the way science, business, healthcare, government, are carried out. The growing use of computers and of the internet in many aspects of our lives generate a deluge of data that conventional techniques are not capable of handling. 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. The emphasis in all units is on concepts, insights and skills that enable graduates to use current technologies and also evaluate and adapt to new technologies as they emerge. Central to the learning of the conceptual material is extensive practical experience where non-trivial problems are analysed, solutions designed, both individually and in groups. Students will undertake a range of core units that introduce databases concepts and technologies, programming and problem solving skills, and statistics.
Graduate Capabilities	The Graduate Capabilities Framework articulates the fundamentals that underpin all of Macquarie’s academic programs. It expresses these as follows: Cognitive capabilities (K) discipline specific knowledge and skills (T) critical, analytical and integrative thinking (P) problem solving and research capability (I) creative and innovative Interpersonal or social capabilities (C) effective communication (E) engaged and ethical local and global citizens (A) socially and environmentally active and responsible Personal capabilities (J) capable of professional and personal judgement and initiative (L) commitment to continuous learning
Program Learning Outcomes	By the end of this program it is anticipated you should be able to: 1. Extract data from a variety of database systems (K, P) 2. 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. Employ pertinent mathematical/statistical modelling techniques (K, P) 5. Apply machine learning and data mining techniques for analysing both small and large quantities of data (K) 6. Engage with clients and the community in a professional, ethical and socially responsible manner. (C, E, A, J) 7. Communicate ideas and solutions to a range of audiences (C, E) 8. Conduct a data science project applying industry-standard methods and practices, including as part of a team (K, T, P, I, C, J)
Learning and Teaching Methods	The Major in Data Science is designed to prepare graduates as professionals for work in industry, research organisations and academia. The program is intended to meet the Australian Computer Society professional standards for ICT courses which includes the underlying core body of knowledge in IT and the professional and ethical responsibilities relevant to working in the IT industry. The learning activities in the degree are designed to provide opportunities for students to meet all of these standards. The academics involved with this program are active researchers, which enables them to integrate cutting-edge research into the units that they teach. The majority of the units in this program have practical components supported by small-group teaching sessions in our computing laboratories. Some units utilise small groups in which students work in teams to achieve a goal. Communication skills are developed through oral presentations. The theoretical components of units are presented in lectures and develop the underlying theory, in addition to developing analytical and problem solving skills. All units have weekly face-to-face activities. Assignments are used for formative and summative purposes. As knowledge in IT is continually evolving, learning and teaching methods support the capacity for students to become independent learners. The major culminates with a Capstone unit that involves students being part of a small team assigned to an industry partner to carry out an industry relevant project. Students work autonomously under the guidance of academic staff and using industry staff as 'clients'. The project allows students to apply in an integrated manner the knowledge and skills they have developed in their studies on a substantial design, analysis or development problem.
Assessment	Units in the Major in Data Science all have at least three different types of assessment. These assessments are designed not just to test discipline-specific knowledge, but all aspects of professional competency include professional practice, project work, design and communication skills. In addition to formal assessments, students are provided with informal feedback from staff and their peers throughout the semester. Assessment types are very diverse and include: Assignments – test the understanding of a learning outcome by means of small size problems Programming Assignments - allow students to demonstrate their competency in developing programs of varying complexity. Reports and documents – beside essay style questions to analyse and critique different topics they also assess relevant skills involving documentation such as requirements documentation and project plans. Oral presentations - these test students ability to communicate the results of their work Group reports – are used when group projects or group laboratory work is conducted. Final exams - The majority of the units will have a final examination where the ability to synthesize and apply knowledge is assessed. Quizzes and in-class tests assess student learning part-way through the unit and provide feedback to students on learning progress. Tutorial assessment – assess students work in formal tutorial sessions where students receive the support of tutors and other staff
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 For undergraduate RPL information visit www.goto.mq.edu.au/nonschoolrpl For domestic postgraduate RPL information visit www.goto.mq.edu.au/pgrpl International Students For RPL information visit www.mq.edu.au/international/rpl
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. Further information can be found at www.students.mq.edu.au/support/ Campus Wellbeing contact details: Phone: +61 2 9850 7497 Email: campuswellbeing@mq.edu.au www.students.mq.edu.au/support/wellbeing
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	The career opportunities lie in the area of data science and more broadly in data analytic and data mining. There is currently significant demand for employees in these areas, often identified by the keyword "Big Data". Potential employers of data science practitioners include the following industries: IT, consulting, retail, automotive, and hospital/medical.
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.

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

Macquarie University

2018 Handbook

Major: Data Science

DAS18V1

This major must be completed as part of an award. The general requirements for the award must be satisfied in order to graduate.

Requirements for the Major:

Credit points

100 level

200 level

300 level

TOTAL CREDIT POINTS REQUIRED TO SATISFY THIS MAJOR

2018 Unit Information

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