Statistical Modelling for Data Science (MAST90139)
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 1 |
|---|---|
| Fees | Look up fees |
Statistical models are central to data science applications. Modelling approaches such as linear and generalized linear models, mixed models, and non-parametric regression are developed. Applications to time series, longitudinal, and spatial data are discussed. Methods for causal inference and handling missing data are introduced.
Intended learning outcomes
On completion of this subject, students will be able to:
- Understand the underlying statistical modelling framework and the limitations of such models.
- Develop statistical models for data coming from diverse settings.
- Appropriately handle data related issues such as missing and incomplete data in a rigorous and justifiable manner.
- Understand how to model dependencies in data such as those that occur in time and spatial domains.
- Pursue further studies in this and related areas, or to be work ready as an applied statistician or a data scientist.
Generic skills
Students will be provided with the opportunity to practise and reinforce:
- 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
Both of:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
MAST90084
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 20 pages of written assignments (equivalent to approx. 20 hours)
| Early in the teaching period | 15% |
Up to 20 pages of written assignments (equivalent to approx. 20 hours)
| Mid semester | 15% |
Up to 20 pages of written assignments (equivalent to approx. 20 hours)
| Late in the teaching period | 15% |
Written examination
| During the examination period | 55% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Guoqi Qian Mode of delivery On Campus (Parkville) Contact hours 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week. Total time commitment 170 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020 Semester 1 contact information
Last updated: 3 November 2022
Further information
- Texts
Last updated: 3 November 2022