CourseMaster's Degree (Coursework)MDataSci

C6004 Master of Data Science

Faculty of Information Technology

Master of Data Science (C6004) is a 2 years full-time, 96-credit-point, master's degree (coursework) course from the Faculty of Information Technology, taught at Clayton. Map your units semester by semester with the MonMap planner.

Credit points
96
Duration
2 years full time
4 years part time
Campus
Clayton
On campus

This is the 2023 handbook entry. See the 2027 entry.

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Requisite map

Overview

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 engineering and big data processing. The course covers topics in both theoretical and practical perspectives, which include statistical machine learning, exploratory analysis, data formats and types, processing of structured and semi-structured data sets, and their role and impact in an organisation and society.

You will be able to apply your learning, knowledge and skills as part of the assessment process and have the opportunity to complete either a research project or an industry experience studio project in a team.

Course structure

Part A. Foundations for advanced data science studies24 credit points
Part B. Core master's studies48 credit points
Part C. Advanced practice24 credit points
You must complete one of the following options

a. Industry experience option

24 credit points
You must complete the following units

FIT level 5 unit

6 credit points
You must complete one FIT-coded level 5 unit (6 credit points). You must have the required prerequisites for the unit you choose.

b. Masters thesis research option

24 credit points
NOTE 1: Enrolment in the research units is dependent on available supervisors and projects. If eligible you will be ranked based on your entire academic record and assessed for suitability to undertake the research component of this program.

NOTE 2: To be eligible for the research option, you must have successfully completed 24 credit points of level 5 FIT-coded units and have achieved an overall average of at least 75% across four completed FIT-coded level 5 units, and have achieved at least a distinction in FIT5125 IT research methods. If you are undertaking a single semester project you must have achieved an overall average of at least 80% across four completed FIT-coded level 5 units.

NOTE 3: For research projects in Bioinformatics, you must have successfully completed 24 credit points of level 5 units, and have achieved an overall average of at least 75% across all completed level 5 units, and have achieved at least a distinction (70%) in FIT5125 IT research methods and BMS5021 Introduction to bioinformatics. (The Bioinformatics option is not available in Vietnam.)
The handbook's description of this structure

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

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. 

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 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 first option. 
  • a program of coursework involving advanced study and an industry experience studio project. 

Master's entry points

Depending on prior qualifications you may receive entry level credit which determines your point of entry to the course:

  • If you are admitted at entry level 1 you complete 96 credit points, comprising Part A, Part B and Part C.
  • If you are admitted at entry level 2 you complete 72 credit points, comprising Part B and Part C.

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.

Course progression map

The course progression map provides guidance on unit enrolment for each semester of study.

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).

Units are 6 points unless otherwise stated.

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

You must complete four units (24 points):

  • FIT9132 Introduction to databases
  • FIT9136 Algorithms and programming foundations in Python
  • FIT9137 Introduction to computer architecture and networks
  • MAT9004 Mathematical foundations for data science

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

You must complete:

a. seven units (42 points)

  • FIT5057 Project management
  • FIT5125 IT research methods
  • FIT5145 Introduction to data science
  • FIT5147 Data exploration and visualisation
  • FIT5196 Data wrangling
  • FIT5197 Statistical data modelling
  • FIT5202 Data processing for big data

b. one unit (6 points) selected from:

  • FIT5149 Applied data analysis
  • FIT5201 Machine learning
  • FIT5212 Data analysis for semi-structured data
  • FIT5230 Malicious AI
  • BMS5021 Introduction to bioinformatics
  • BMS5022 Advanced bioinformatics: techniques for efficient genome, transcriptome and proteome analysis

Part C. Advanced practice (24 points)

You must complete either a. or b. below:

a. Industry experience:

  • FIT5120 Industry experience studio project (12 points)
  • FIT5122 Professional practice
  • one FIT level 5 unit (6 points)

b. Minor thesis research:*

  • FIT5126 Masters thesis part 1
  • FIT5127 Masters thesis part 2
  • FIT5128 Masters thesis final
  • FIT5122 Professional practice

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.

* To be eligible for the research option, you must have successfully completed 24 credit points of level 5 FIT-coded units and have achieved an overall average of at least 75% across four completed FIT-coded level 5 units, and have achieved at least a distinction (70%) in FIT5125 IT research methods. If you are undertaking a single semester project you must have achieved an overall average of at least 80% across four completed FIT-coded level 5 units.

* For research projects in Bioinformatics, you must have successfully completed 24 points of level 5 units, and have achieved an overall average of at least 75% across all completed level 5 units, and have achieved at least a distinction in FIT5125 IT research methods and BMS5021 Introduction to bioinformatics.

Learning outcomes

These course outcomes are aligned with the Australian Qualifications Framework and Monash Graduate Attributes.

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

  1. 1

    analyse the lifecycle of data through an organisation

  2. 2

    apply the major theories in the field of data analysis and data exploration to some characteristic problems

  3. 3

    plan, manage and execute a data science project individually and collaboratively on a new application area using knowledge of the data lifecycle and analysis process

  4. 4

    investigate, analyse, document and communicate the core issues and requirements in developing data analysis capability in a global organisation

  5. 5

    demonstrate an understanding of data science to a level of depth and sophistication consistent with senior professional practice

  6. 6

    review, synthesise, apply and evaluate contemporary data science theories through independent research and a research thesis, or by utilising research methods for scholarly or professional purposes.

  7. 7

    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.

Entry requirements

English language

Monash Level A, that is: IELTS (Academic): 6.5 overall (no band lower than 6.0); or Pearson Test of English (Academic): score of 58 overall with no band lower than 50; or TOEFL Internet-based test: score of 79 overall with minimum scores: Writing: 21, Listening: 12, Reading: 13 and Speaking: 18; or Equivalent approved English test

More information

Progression to further studies

Successful completion of this course may provide a pathway to a graduate research degree.

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

Professional accreditation

This course is accredited by the Australian Computer Society (ACS) as meeting the standard of knowledge for professional-level membership.

Other information

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 engineering and big data processing. The course covers topics in both theoretical and practical perspectives, which include statistical machine learning, exploratory analysis, data formats and types, processing of structured and semi-structured data sets, and their role and impact in an organisation and society.

You will be able to apply your learning, knowledge and skills as part of the assessment process and have the opportunity to complete either a research project or an industry experience studio project in a team.

Contacts

Academic Coordinator
Dr Jackie Rong

Common questions

How long is Master of Data Science?

2 years full time, 96 credit points. At 24 credit points a semester, that is 4 semesters of full-time study.

Where can I study Master of Data Science?

At Clayton.

How do I plan my Master of Data Science units?

Open the course in the MonMap planner. It lays out your semesters, checks prerequisites as you drag units in, and tracks the credit points each requirement still needs.

Course details

Qualification
Master's Degree (Coursework)
AQF level
Level 9
Credit points
96
Full time
2 Years
Part time
4 Years
Maximum time
6 years
Faculty
Faculty of Information Technology
CRICOS code
085349A
Abbreviation
MDataSci
Award title
Master of Data Science