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 and Malaysia. Map your units semester by semester with the MonMap planner.
- Credit points
- 96
- Duration
- 2 years full time
- 4 years part time
- Campus
- Clayton, Malaysia
- On campus
This is the 2024 handbook entry. See the 2027 entry.
Reviews
No reviews yetNo reviews yet. Be the first to review C6004.
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
a. Core units
42 credit points- FIT5057Project managementNo reviews yet6 cp
- FIT5125IT research methodsNo reviews yet6 cp
- FIT5145Foundations of data scienceNo reviews yet6 cp
- FIT5147Data exploration and visualisationNo reviews yet6 cp
- FIT5196Data wranglingNo reviews yet6 cp
- FIT5197Statistical data modellingNo reviews yet6 cp
- FIT5202Data processing for big dataNo reviews yet6 cp
b. Additional data science unit
6 credit pointsPart C. Advanced practice24 credit points
a. Industry experience option
24 credit points- FIT5120Industry experience studio projectNo reviews yet12 cp
- FIT5122Professional practiceNo reviews yet6 cp
FIT level 5 unit
6 credit pointsYou must have the required prerequisites for the unit you choose.
b. Master's thesis research option
24 credit pointsNote 1: 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 2: To be eligible for the research option, you must have an overall average WAM of at least 65% and 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.
Note 3: For research projects in Bioinformatics, you must have an overall average WAM of at least 65% and 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 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 (Clayton) (Malaysia) 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
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) or BMS5022 Advanced bioinformatics: techniques for efficient genome, transcriptome and proteome analysis
b. Master's 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.
Note: To be eligible for the research option, you must have an overall average WAM of at least 65% and 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.
Note: For research projects in Bioinformatics, you must have an overall average WAM of at least 65% and 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 (70%) 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
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, 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
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, 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
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 provisionally accredited by the Australian Computer Society (ACS) as meeting the standard of knowledge for professional-level membership. The Faculty is in the process of obtaining full accreditation.
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 Lim Chern Hong
- 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 and Malaysia.
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.