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Statistical Modelling for Data Science (MAST90139)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
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: 31 January 2024
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
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 |
OR
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
OR
Admission into one of:
• Master of Data Science (MC-DATASC)- Statistics Background Stream
• Master of Data Science (MC-DATASC)- Data Science Background Stream
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: 31 January 2024
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: 31 January 2024
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 28 February 2022 to 29 May 2022 Last self-enrol date 11 March 2022 Census date 31 March 2022 Last date to withdraw without fail 6 May 2022 Assessment period ends 24 June 2022 Semester 1 contact information
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
Last updated: 31 January 2024