Indicative annual fees are based on a full year full time load (80 units). Find out more about fees
Program handbook
Description
Advances in the scientific and technological sectors have caused an enormous explosion in the volume of data generated. As the volume and complexity of data increases, a workforce with skills and qualifications in data management and analysis will be in greater demand. In recent years, data scientist/analyst and related careers have been consistently ranked among the top jobs and most in-demand skills requested by employers.
The University of Newcastle's Master of Data Science will equip students with the essential professional skills and knowledge to understand and engage with a variety of data generated from diverse industries and settings. Graduates will learn how to manage, analyse, interpret, visualise and effectively communicate data. They will also learn how to use data to model real-word situations.
The program is professional in nature and industry oriented. Students will learn to understand and explore data using applied problem solving and computer practice. All learning will be underpinned by the required theoretical knowledge.The core courses cover foundational areas, such as data wrangling and visualisation, databases, predictive analytics, big data, and statistics.
Students will complete a specialisation in either Computational Intelligence or Health Data Analysis. The Computational Intelligence specialisation is aligned to a computer science approach to data science, which includes further studies in data mining and machine learning. The Health Data Analysis specialisation focuses on medical statistics, biostatistics, and related areas, while providing transferrable data analysis skills also applicable to other domains.
Please note that students who commence in Semester 1 can complete the program in a minimum of 2 years full time. Students who commence in Semester 2 can complete the program in a minimum of 1.5 years full time.
Program learning outcomes
On successful completion of the program students will have:
- Specialised knowledge of statistical and computational models and concepts and proficiency in their application.
- Specialist knowledge in statistical and computational techniques for analysing and interpreting data sets.
- Critical thinking and analytical problem solving to support data management and data-oriented decisions.
- Specialised knowledge and skills required to use contemporary Big Data technologies to store, manage, process and analyse large structured or unstructured data sets.
- Effective independent and collaborative work skills to apply specialised knowledge and expert judgement to data science.
Details
Information correct as at | Mar 29, 2024 5:44 pm |
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Program code | 40112 |
AQF level | Level 9 Masters Degree (Coursework) |
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Term type | Semesters and Trimesters |
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Admission information
Admission requirements
- Students with a three year, Bachelor degree (AQF level 7) or higher in a different discipline area (also called non-cognate discipline) to data science and their chosen specialisation are eligible to undertake the 120 unit program which can be completed in 2 years full time (commencing Semester 1), 1.5 years full time (commencing Semester 2) or up to 5 years part time.
- Students with a three year Bachelor degree (AQF level 7) or higher in a non-cognate discipline who also hold a minimum of five years demonstrable Recognised Prior Learning (RPL) in an area related to data science and/or their chosen specialisation (also called cognate discipline) may be eligible to study between 80 and 120 units, inclusive.
- Students with a three year Bachelor degree (AQF level 7) or Bachelor (Honours) (AQF 8) in a cognate discipline to data science and/or their specialisation may be eligible to study between 80 and 120 units, inclusive.
- Students with a Graduate Certificate or Graduate Diploma (AQF level 8) in a cognate discipline to data science and/or data analytics may be eligible to undertake an 80 unit program which can be completed in 1 year full time or part time equivalent.
English Language Requirements
All Applicants must demonstrate that they meet the University’s English proficiency requirement. Further information regarding English language proficiency requirements can be found at the English Language Proficiency for Admission Policy here.
- IELTS Overall Minimum: 6.5
- IELTS Sub Test Minimum: 6
Credit transfer
If you wish to apply for credit for studies completed at another institution, or if you are changing programs within the University and wish to transfer your credit to the new program, visit the University's credit website for more information on applying for credit.
Academic requirements for program completion
Total units required
120 units
Program duration
1.5 years full-time or part-time equivalent up to 5 years maximum.
Program requirements
Please note that students who commence in Semester 1 can complete the program in a minimum of 2 years full time. Students who commence in Semester 2 can complete the program in a minimum of 1.5 years full time.
Students are required to complete a total of, but no more than, 120 units, comprised of the following:
- 40 units of Core courses
- 10 units of courses chosen from the Course List.
- One 60 unit Specialisation
- 10 unit Elective
Please note: Students must only complete courses that form part of the program.
Program planner
Current students program planner
Current students can plan their program using Program Planner.
Prospective student degree planner
If you're a prospective student considering studying this degree take a look at My Degree Planner to see what your study journey will look like.
Additional documents
- 2021 Semester 1 Transition Arrangements 168.1 KB
- 2021 Semester 2 Transition Arrangements - Computational Specialisation 156.2 KB
Transition arrangements
2021 Revision
From Semester 2 2021, the Computational Intelligence Specialisation has been updated to remove INFT6304 and SENG6051 as directed courses. Students who are completing this specialisation and commenced prior to Semester 2 2021 can count INFT6304 or SENG6051 as their directed course. Students who commence from Semester 2 2021 cannot count INFT6304 or SENG6051 as their directed course.
Program structure
Students can plan their program using Program Planner. It is recommended students review the program handbook in conjunction with using Program Planner.
Advanced course filters
Courses that are currently unavailable are hidden by default. You can show them by adjusting the advanced course filters above, or clicking the 'show all' links below each section.
Core
Complete the following core courses to fulfil the requirements of this program. | |||
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Code | Title | Term / Location | Units |
COMP6140 | Databases and Information Management |
| 10 units |
INFT6201 | Big Data |
| 10 units |
STAT6001 | Data Wrangling and Visualisation |
| 10 units |
STAT6020 | Predictive Analytics |
| 10 units |
Course List
Complete 10 units from the following course list. | |||
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Code | Title | Term / Location | Units |
STAT6160 | Data Analytics for Business Intelligence |
| 10 units |
STAT6170 | The Science of Data Interrogation |
| 10 units |
Specialisation
Computational Intelligence
Compulsory | |||
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Complete the following compulsory courses. | |||
Code | Title | Term / Location | Units |
COMP6340 | Data Mining |
| 10 units |
COMP6380 | Machine Intelligence |
| 10 units |
COMP6900 | Computing Project |
| 20 units |
INFT6800 | Professional Practice in IT |
| 10 units |
Course List | |||
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Complete 10 units from the following course list. | |||
Code | Title | Term / Location | Units |
COMP6230 | Algorithms |
| 10 units |
COMP6350 | Advanced Database |
| 10 units |
INFT6009 | Cloud Computing and Mobile Applications for the Enterprise |
| 10 units |
SENG6110 | Object Oriented Programming |
| 10 units |
SENG6120 | Data Structures |
| 10 units |
Health Data Analysis
Compulsory | |||
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Complete the following compulsory courses. | |||
Code | Title | Term / Location | Units |
BIOS6070 | Linear Regression Modelling |
| 10 units |
BIOS6940 | Generalised Linear Models |
| 10 units |
BIOS6990 | Applied Longitudinal Analysis |
| 10 units |
PUBH6303 | Applied Research |
| 10 units |
Course List | |||
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Complete 20 units from the following course list. | |||
Code | Title | Term / Location | Units |
BIOS6061 | Clinical Trial Design |
| 10 units |
BIOS6170 | Probability and Biostatistical Inference |
| 10 units |
EPID6420 | Fundamentals of Epidemiology |
| 10 units |
EPID6430 | Advanced Epidemiology |
| 10 units |
Electives
Electives 10 unit requirement | |
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Complete 10 units of electives to fulfil the requirements of the program. Electives can be used to extend and complement your core studies with more courses in the same field of study, or from areas that might be of interest to you. Electives can be chosen from all postgraduate courses available at the University that do not have any other conditions (such as a course requisite) applied to them. | 10 units |
Additional information
Through the Pathways and Academic Learning Support Centre, students can access a free suite of NUPrep preparation courses as well as Academic Learning Support.
Aboriginal and Torres Strait Islander students can draw on the assistance and support provided by the Indigenous Student Support and Development service through the Wollotuka Institute.
International students
All International Students enrolled in the program will be provided with an orientation to familiarise them with the rules, expectations, facilities and services offered by the University. Please visit our International Students website to find out more about the support services available to international students.
Additional support
AccessAbility provides advice and reasonable adjustments to Students with a medical or health condition or disability. If you require adjustments to undertake your program, contact the Student Support Advisors - AccessAbility before semester or early in the semester. They will work with the College or School to ensure that this happens in a timely manner.
Please note: All students must fulfil the inherent requirements of the programs and courses they are undertaking. While reasonable adjustments can be made, these adjustments cannot compromise academic integrity. It is the student's responsibility to check all the requirements of courses, and consider the effects of any medical condition or disability on their ability to complete course requirements. More information is available online.
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