C04372v3 Master of Data Science and Innovation
University of Technology Sydney
About
The Master of Data Science and Innovation is a world-leading program of study in analytics and data science.Taking a transdisciplinary approach, the course utilises a range of perspectives from diverse fields and integrates them with industry experiences, real-world projects and self-directed study, equipping graduates with an understanding of the potential of analytics to transform practice.
The course is delivered in a range of modes, including contemporary online and face-to-face learning experiences in UTS's leading-edge facilities.Industry partnerships and engagement are a core part of the course.
The course curriculum and subjects are co- designed and developed by UTS academic data experts and industry partners, and regularly reviewed and updated to keep up with the current market needs and latest data science trends.
During the course study, students have abundant opportunities working on real world data sets/projects.This course has been developed as a response to a global talent gap for people with data science knowledge, as identified and reported by the McKinsey Global Institute study (2011).
The study predicted a shortfall by 2018 of nearly 200,000 data scientists and 1.5 million managers with the capability to make decisions using big data in the United States alone.The dramatic growth of data in every conceivable industry, from oceanography to market research, presents another major driving force in generating unprecedented global demand for data science skills.
Structure
Student must complete 96 credit points (CP), comprising 44CP core subjects, 32CP specified data science related optional subjects and 20CP elective subjects. Elective subjects can be selected from data science related subjects and from across the University’s disciplines. Enrolment in subjects from other disciplines is dependent on approval from the Course Director and subject coordinator, and usually requires demonstrated ability to meet pre-requisites. This flexible course structure enables students to pursue their own particular interests and career aspirations.
Students who have completed certain components of this course may qualify for a Graduate Certificate in Data Science and Innovation (C11274) or Graduate Diploma in Data Science and Innovation (C06124).
Industrial training/professional practice
The iLabs and internship projects provide the opportunity for students to design investigations utilising contemporary data discovery techniques and large, complex, multi-structure data sets. The study can focus on the student's current work environment, or industry placements can be negotiated in a discipline of interest.
Course completion requirements
| course | credit |
|---|---|
| STM91478 Data Science Core Subjects | 44cp |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp |
| CBK91916 Electives | 20cp |
| Total | 96cp |
Course program
The following example shows a typical full-time program.
Autumn commencing, full time
Year 1
Autumn session
| course | credit |
|---|---|
| 36100 Data Science for Innovation | 8cp |
| 36106 Machine Learning Algorithms and Applications | 8cp |
| Select 6 credit points from the following: | 6cp |
| CBK91916 Electives | |
Spring session
| course | credit |
|---|---|
| 94692 Data Science Practice | 8cp |
| 36101 Leading Data Science Initiatives | 8cp |
| 36103 Statistical Thinking for Data Science | 8cp |
Year 2
Autumn session
| course | credit |
|---|---|
| 36104 Data Visualisation and Narratives | 8cp |
| 94691 Deep Learning | 8cp |
| Select 8 credit points from the following: | 8cp |
| CBK91916 Electives | |
Spring session
| course | credit |
|---|---|
| 36105 iLab 2 | 12cp |
| 36109 Data and Decision Making | 8cp |
| Select 6 credit points from the following: | 6cp |
| CBK91916 Electives | |
Spring commencing, full time
Year 1
Spring session
| course | credit |
|---|---|
| 36101 Leading Data Science Initiatives | 8cp |
| 36103 Statistical Thinking for Data Science | 8cp |
| 94692 Data Science Practice | 8cp |
Year 2
Autumn session
| course | credit |
|---|---|
| 36106 Machine Learning Algorithms and Applications | 8cp |
| 36104 Data Visualisation and Narratives | 8cp |
| 36100 Data Science for Innovation | 8cp |
Spring session
| course | credit |
|---|---|
| 36105 iLab 2 | 12cp |
| 36109 Data and Decision Making | 8cp |
| Select 6 credit points from the following: | 6cp |
| CBK91916 Electives | |
Year 3
Autumn session
| course | credit |
|---|---|
| 94693 Big Data Engineering | 8cp |
| Select 14 credit points from the following: | 14cp |
| CBK91916 Electives | |
Entry requirements
Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.
All applicants must satisfy the following requirement:
- bachelor degree, or higher qualification, in a relevant discipline, such as information technology, mathematical sciences, physics and astronomy, engineering, accounting, business and management, banking, finance and related fields, or economics and econometrics.
If the applicant's academic qualification is not listed in the above disciplines but they do have at least two years full time work experience in data analytics, database management or programming related fields, then they must also provide:
- a C.V. outlining work experience and education, as well as other relevant evidence and information and
- an official Statement of Service, from the employer, confirming the dates of employment, and a description of the position held within the organisation.
The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64; or CAE: 176-184.
Eligibility for admission does not guarantee offer of a place.
International students
Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.
Institution
