Statistical Modelling for Data Science (MAST90139)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
Overview
| Availability | Semester 1 - Dual-Delivery |
|---|---|
| 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
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; and
- Time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
All of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90104 | A First Course In Statistical Learning | Semester 2 (Dual-Delivery - Parkville) |
25 |
| MAST90105 | Methods of Mathematical Statistics | Semester 1 (Dual-Delivery - Parkville) |
25 |
Option 2
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90084 | Statistical Modelling | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
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
| Description | Timing | Percentage |
|---|---|---|
Continuing assessment of up to 60 hours work, worth 45% of the mark, throughout the semester
| Throughout the teaching period | 45% |
Written examination
| During the examination period | 55% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Guoqi Qian Mode of delivery Dual-Delivery (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 1 March 2021 to 30 May 2021 Last self-enrol date 12 March 2021 Census date 31 March 2021 Last date to withdraw without fail 7 May 2021 Assessment period ends 25 June 2021 Semester 1 contact information
Last updated: 3 November 2022
Further information
- Texts
Last updated: 3 November 2022