Master of Data Science - 2018

The Master of Data Science prepares you for a career in data science giving you the skills needed to deal effectively within the areas of data analysis, data management or big data processing. The course includes topics in statistical and exploratory analysis, data formats and languages, processing of massive data sets, management of data and its role and impact in an organisation and society.

The course has two streams to choose from:

• Data science - a broader range of units related to data science
• Advanced data analytics - more depth in data analysis and machine learning.

In either stream you will be able to apply your learning to your own context as part of the assessment process and have the opportunity to complete either a research project or an industry experience studio project.

These course outcomes are aligned with the Australian Qualifications Framework level 9 and Monash Graduate AttributesAustralian Qualifications Framework level 9 and Monash Graduate Attributes (http://monash.edu.au/pubs/handbooks/alignmentofoutcomes.html).

Upon successful completion of this course it is expected that graduates will be able to:

1. analyse the lifecycle of data through an organisation
2. apply the major theories in the field of data analysis and data exploration to some characteristic problems
3. plan a data science project on a new application area using knowledge of the data lifecycle and analysis process
4. investigate, analyse, document and communicate the core issues and requirements in developing data analysis capability in a global organisation
5. demonstrate an understanding of data science to a level of depth and sophistication consistent with senior professional practice
6. review and evaluate data science projects
7. review, synthesise, apply and evaluate contemporary data science theories through either a significant research thesis component or research-grounded industrial project
8. document and communicate ethical and legal issues and norms in privacy and security, and other areas of community impact with regards to the practice of data science.

The course is structured in three parts: Part A. Foundations for advanced data science studies, Part B. Core master's study, and Part C. Advanced practice. All students complete Part B. Depending upon prior qualifications, you may receive credit for Part A or Part C or a combination of the two.

Note that if you are eligible for credit for prior studies you may elect not to receive the credit.

Part A. Foundations for advanced data science studies

These studies will provide an orientation to the field of data science at graduate level. They are intended for students whose previous qualification is not in a cognate field.

Part B. Core master's study

These studies draw on best practices within the broad realm of data science practice and research. You will gain a critical understanding of theoretical and practical issues relating to data science. Your study will focus on your choice either of data science or advanced data analytics.

Part C. Advanced practice

The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. You have two options:

• a program of coursework involving advanced study and an industry experience studio project.
• a research pathway including a thesis. If you wish to use this master's course as a pathway to a higher degree by research you should take this second option.

If you are admitted to the course with a recognised honours degree in a discipline cognate to data science, will receive credit for Part C, however, should you wish to complete the research project option as part of the course you should consult with the course coordinator.

The course comprises 96 points structured into three parts: Part A. Foundations for advanced data science studies (24 points), Part B. Core master's study (48 points) and Part C. Advanced practice (24 points).

If you are admitted at:

• entry level 1 you complete 96 points, comprising Part A, Part B and Part C
• entry level 2 you complete 72 points, comprising Part B and Part C or Part A and Part B
• entry level 3 you complete 48 points, comprising Part B.

Note: If you are eligible for credit for prior studies you may elect not to receive the credit and complete one of the higher credit-point options.

Units are 6 credit points unless otherwise stated.

Part A. Foundations for advanced data science studies (24 points)

You must complete:

a. three units (18 points):

• FIT9132 Introduction to databases
• FIT9133 Programming foundations in Python
• MAT9004 Mathematical foundations for data science

b. one unit (6 points) from:

• FIT9123 Introduction to business information systems
• FIT9134 Computer architecture and operating systems

Part B. Core master's study (48 points)

You must complete:

a. three units (18 points):

• FIT5145 Introduction to data science
• FIT5196 Data wrangling
• FIT5197 Modelling for data analysis

Data science stream

b. four units (24 points) selected from:

• FIT5097 Business intelligence modelling
• FIT5146 Data curation and management
• FIT5147 Data exploration and visualisation
• FIT5148 Distributed databases and big data
• FIT5149 Applied data analysis
• FIT5202 Data processing for big data
• FIT5205 Data in society
• FIT5206 Digital continuity

c. one elective unit (6 points) selected from:

• the data science stream above
• the approved data science elective list below
• any FIT-coded level 5 unit
• any level 5 unit offered by any other faculty of the University with faculty approval, if you have the required prerequisites and there are no restrictions on enrolment*

Advanced data analytics stream

b. four units (24 points):

• FIT5147 Data exploration and visualisation
• FIT5148 Distributed databases and big data or FIT5202 Data processing for big data
• FIT5149 Applied data analysis
• FIT5201 Data analysis algorithms

c. one unit (6 points) selected from:

• the approved data science elective list below
• any FIT-coded level 5 unit
• any level 5 unit offered by any other faculty of the University with faculty approval, if you have the required prerequisites and there are no restrictions on enrolment*

* Units can be identified using the browse units toolbrowse units tool (http://www.monash.edu.au/pubs/handbooks/units/search) and indexes of unitsindexes of units (http://www.monash.edu.au/pubs/handbooks/units/) in the current editioncurrent edition (http://www.monash.edu.au/pubs/handbooks) of the Handbook. The level of the unit is indicated by the first number in the unit code. You may need permission from the owning faculty to enrol in units taught by other faculties.

Data science electives list

• FIT5046 Mobile and distributed computing systems
• FIT5047 Intelligent systems
• FIT5057 Project management
• FIT5088 Information and knowledge management systems
• FIT5097 Business intelligence modelling
• FIT5106 Information organisation
• FIT5107 Managing business records
• FIT5108 Reading unit (approval required)
• FIT5109 Research topic (approval required)
• FIT5139 Advanced distributed and parallel systems
• FIT5146 Data curation and management
• FIT5166 Information retrieval systems
• FIT5195 Business intelligence and data warehousing
• FIT5201 Data analysis algorithms
• FIT5202 Data processing for big data
• FIT5204Not offered in 2018 Heritage informatics
• FIT5205 Data in society
• FIT5206 Digital continuity
• FIT5207Not offered in 2018 Data for sustainability
• FIT5211 Algorithms and data structures
• FIT5212Not offered in 2018 Data analysis for semi-structured data

Note: Not all units will be offered every year.

Part C. Advanced practice (24 points)

You must complete either a. or b. below:

a. Minor thesis research:**

• FIT5125 IT research methods
• FIT5126 Masters thesis part 1
• FIT5127 Masters thesis part 2
• FIT5128 Masters thesis final

Enrolment in the research units is dependent on available supervisors and projects. Eligible students will be ranked based on their entire academic record and assessed for suitability to undertake the research component of this program.

** Note: To be eligible for the research option, you must have successfully completed 24 points of level five FIT units and have achieved an overall average of at least 75 per cent across all completed FIT level five units.

b. Industry experience:

• FIT5120 Industry experience studio project (12 points)
• FIT5122 Professional practice
• Data science stream: one additional elective unit from the approved data science elective list in Part B,c.
• Advanced data analytics stream: FIT5213 Advanced data analytics case study

You may exit this course early and apply to graduate with one of the following awards, provided you have satisfied the requirements indicated for that award during your enrolment in this master's course:

• Graduate Certificate of Data Science after successful completion of 24 credit points of study, including FIT5145 (Introduction to data science), FIT5196 (Data wrangling) and FIT5197 (Modelling for data analysis) and one unit (6 points) from Part A or Part B
• Graduate Diploma of Data Science after successful completion of 48 credit points of study including FIT5145 (Introduction to data science), FIT5196 (Data wrangling), FIT5197 (Modelling for data analysis) and five units (30 points) from Part A or Part B (with a maximum of 12 points from Part A).

Progression to a Faculty of Information Technology higher degree by research will be conditional on you completing the minor thesis research pathway (as described in Part C, a.) and achieving the minimum entry requirements for either 3337 Master of Philosophy or 0190 Doctor of Philosophy.