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Data Science is an inter-disciplinary field, drawing on mathematics, statistics, and computer science, that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data Science is a significant area of growth and potential employment in Australia and the Asia-Pacific region. This course provides the necessary foundations in the disciplines of mathematics, statistics and computer science, and develops student knowledge and skills in some of the key tools and techniques relevant to data science. It also pays specific attention to ethical issues surrounding the manner in which data is gathered, stored, analysed and used.
This course information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester. In particular please check the course requirements and the unit and unit set offerings, as these differ according to course delivery location.
In the final semester of the course, students will complete a project involving integrated learning with a company, agency, or university academic in their discipline area.
Each project will have an agreement for student placement. Each workplace will be inspected, and the appropriate forms completed indicating it is a safe work environment for students. Every student will be required to complete a risk assessment and management plan as part of this placement.
Students will be expected to participate in a minimum of 456 hours working with an organisation on a project and produce a report on activities.
WIL will be undertaken by students who have been awarded a work experience placement.
Students will not be able to complete the course.
English competency requirements may be satisfied through completion of one of the following:
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| CSI6208 | Programming Principles | 20 |
| MAT5212 | Biostatistics | 20 |
| MAT6105 | Mathematical Fundamentals | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| Students who commence the course midyear will complete their second and third semesters in reverse order. | ||
| MAT6104 | Applied Multivariate Statistics | 20 |
| SCI6120 | Science Communication and Ethics | 20 |
| CSI6209 | Artificial Intelligence | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| MAT6206 | Data Analysis and Visualisation | 20 |
| CSI6207 | Systems Analysis and Database Design | 20 |
| MAT6100 | Time Series Forecasting | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| SCI6108 | Postgraduate Science Project | 60 |
For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.
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Data Science is an inter-disciplinary field, drawing on mathematics, statistics, and computer science, that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data Science is a significant area of growth and potential employment in Australia and the Asia-Pacific region. This course provides the necessary foundations in the disciplines of mathematics, statistics and computer science, and develops student knowledge and skills in some of the key tools and techniques relevant to data science. It also pays specific attention to ethical issues surrounding the manner in which data is gathered, stored, analysed and used.
This course information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester. In particular please check the course requirements and the unit and unit set offerings, as these differ according to course delivery location.
Students have the opportunity to spend their final semester undertaking a professional placement embedded within a host organisation. Successful applicants for this capstone placement opportunity will enrol in and complete SCI6700 Professional Placement (Science and Mathematics).
Students, the WIL host organisation and the school's WIL Coordinator must complete a WIL documentation pack (which includes all required OSH and Risk Assessment documents) before the placement can commence. WIL host organisations may have additional clearance requirements of applicants, including evidence of police clearance, non-disclosure agreements or research ethics clearances. There may also be vaccination or other similar requirements, including those imposed by government or third-party placement hosts, that apply to Professional Placements which form part of your course. Please consider this requirement before applying for Professional Placement and speak with the WIL and Course Coordinator if this raises any concerns. You may not be able to complete the Professional Placement unit if you are unable to meet the placement requirements.
Students are required to complete a placement which is equivalent to one semester of full-time study. Whilst attendance is negotiated with the WIL host organisation, typically students will be expected to undertake a minimum of 300 hours over a maximum of 17 weeks. Typical full-time placements usually comprise 450 hours of professional placement.
Students are required to apply in writing to the Work Integrated Learning Coordinator (the student's Course Coordinator can advise who is the responsible staff member) upon successful completion of 120 credit points of study. Students should seek the advice of their Course Coordinator and the WIL Coordinator as to the appropriateness of pursuing the work placement option within their course structure. Successful applicants will need to complete any directed pre-placement preparation activities.
Students will need permission from the WIL Coordinator to enrol in SCI6700 Professional Placement (Science and Mathematics).
Where students do not successfully complete SCI6700 they will be required to enrol in SCI6108 the following semester.
English competency requirements may be satisfied through completion of one of the following:
This course has been accredited by ECU as an AQF Level 9 Masters Degree (Coursework) Award.
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| CSI6208 | Programming Principles | 20 |
| MAT5212 | Biostatistics | 20 |
| MAT6105 | Mathematical Fundamentals | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| Students who commence the course midyear will complete their second and third semesters in reverse order. | ||
| MAT6104 | Applied Multivariate Statistics | 20 |
| SCI6120 | Science Communication and Ethics | 20 |
| CSI6209 | Artificial Intelligence | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| MAT6206 | Data Analysis and Visualisation | 20 |
| CSI6207 | Systems Analysis and Database Design | 20 |
| MAT6100 | Time Series Forecasting | 20 |
| Unit Code | Unit Title | Credit Points |
|---|---|---|
| SCI6108 ^ | Postgraduate Science Project | 60 |
| OR the following unit for students with an approved professional placement | ||
| SCI6700 ^ | Professional Placement (Science and Mathematics) | 60 |
^ Core Option
For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.
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