Practice of Statistics & Data Science (MAST90027)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
This subject builds on methods and techniques learned in theoretical subjects by studying the application of statistics in real contexts. Emphasis is on the skills needed for a practising statistician, including the development of mature statistical thinking, organizing the structure of a statistical problem, the contribution to the design of research from a statistical point of view, measurement issues and data processing. The subject deals with thinking about data in a broad context, and skills required in statistical consulting.
Intended learning outcomes
On completion of the subject, a student should:
- have developed their skills in the effective communication of statistical material, verbally and in writing;
- be aware of the key requirements of working as a practising statistician, and developed their ability to meet these requirements;
- have developed their understanding of the needs and characteristics of users of statistics, and learned about the important issues that arise in interactions between statisticians and their clients.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
One of:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
| MAST30027 | Modern Applied Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
| Description | Timing | Percentage |
|---|---|---|
Up to 75 pages of written assignments (five assignments, two worth 15% each, two worth 25% each and one worth 20% due at approximately equal intervals)
| Throughout the teaching period | 100% |
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Ian Gordon Mode of delivery On Campus (Parkville) Contact hours 36 total, comprising two one-hour lectures and one one-hour practice class per week Total time commitment 170 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 Semester 2 contact information
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Graduate Diploma in Biostatistics Course Master of Data Science Course Master of Biostatistics Course Master of Science (Mathematics and Statistics) Course Master of Commerce (Finance) Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Informal specialisation Mathematics and Statistics - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 3 November 2022