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 2020 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 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.


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.

Course structure

Part A. Foundations for advanced data science studies24 credit points
Part B. Core master's study48 credit points
Part C. Advanced practice24 credit points
You must complete one of the following options

a. Minor 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 all completed FIT-coded level 5 units, and have achieved at least a distinction in FIT5125 IT research methods. 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 in FIT5125 IT research methods and BMS5021 Introduction to bioinformatics.

Elective unit

6 credit points
You must complete one elective unit (6 credit points) from any FIT or BMS-coded level 5 unit, or any level 5 unit offered by any other faculty of the University with faculty approval, if you have the prerequisites and there are no restrictions on enrolments.

b. Industry experience option

24 credit points
You must complete the following units

Elective unit

6 credit points
You must complete one elective unit (6 credit points) from any FIT or BMS-coded level 5 unit, or any level 5 unit offered by any other faculty of the University with faculty approval, if you have the prerequisites and there are no restrictions on enrolments.
The handbook's description of this structure

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 and Part C. Depending upon prior qualifications, you may receive credit for Part A.

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. 

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

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.

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.

If you intend to specialise in Bioinformatics, you must complete the three BMS level 5 units in Part B (18 points) and one BMS level 5 elective unit (6 points) from Part C.

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.

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 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. five units (30 points)

  • FIT5125 IT research methods
  • FIT5145 Introduction to data science
  • FIT5147 Data exploration and visualisation
  • FIT5196 Data wrangling
  • FIT5197 Statistical data modelling

b. three units (18 points) selected from:

  • FIT5149 Applied data analysis
  • FIT5201 Machine learning
  • FIT5202 Data processing for big data
  • FIT5212 Data analysis for semi-structured data
  • BMS5021 Introduction to bioinformatics
  • BMS5022 Advanced bioinformatics: techniques for efficient genome, transcriptome and proteome analysis
  • BMS5xxx Computational network medicine: data integration, analysis and modelling


Part C. Advanced practice (24 points)

You must complete either a. or b. below:

a. Minor thesis research:*

  • FIT5126 Masters thesis part 1
  • FIT5127 Masters thesis part 2
  • FIT5128 Masters thesis final
  • one elective unit (6 points) from any FIT or BMS-coded level 5 unit, or any level 5 unit offered by any other faculty of the University with faculty approval, if you have the prerequisites and there are no restrictions on enrolments.

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 points of level 5 FIT-coded units and have achieved an overall average of at least 75% across all completed FIT-coded level 5 units, and have achieved at least a distinction in FIT5125 IT research methods.

* 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.

b. Industry experience:

  • FIT5120 Industry experience studio project (12 points)
  • FIT5122 Professional practice
  • one elective unit (6 points) from any FIT or BMS-coded level 5 unit, or any level 5 unit offered by any other faculty of the University with faculty approval, if you have the prerequisites and there are no restrictions on enrolments.

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 a data science project 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 minimum: Level A, that is: IELTS: 6.5 overall (no band lower than 6.0); or TOEFL Paper-based test: 550 with a TWE of 4.5; 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, a.) and achieving the minimum entry requirements for either the Master of Philosophy (3337) or the Doctor of Philosophy (0190).

Notes for students

2021 Monash University Global Campus Program
If you're unable to start this course in Australia, due to COVID-19 restrictions, you can start this course on-campus at Monash University Malaysia in semester one 2021, then continue in Australia once restrictions are eased. Find out more.

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 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.


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.

Contacts

Academic Coordinator
Dr Lan Du

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