Computational Statistics & Data Science (MAST90083)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
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Overview
| Availability | Semester 2 - Dual-Delivery |
|---|---|
| Fees | Look up fees |
Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where “Big Data” problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, generalised additive models, classification and regression trees, forward stagewise and gradient boosting models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include the bootstrap resampling and inference, cross-validation, the EM algorithm and Louis method, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs sampler and Metropolis-Hastings algorithm.
Intended learning outcomes
On completion of this subject, students should be able to:
- Explain the role of theory and computing in modern statistics and data science, and how they are implemented in applications;
- Apply nonparametric and Monte Carlo methods in statistics and data science; and
- Develop key knowledge to pursue further studies in this discipline 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;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
OR
Note: the following subject/s can also be taken concurrently (at the same time):
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
| MAST90104 | A First Course In Statistical Learning | Semester 2 (Dual-Delivery - Parkville) |
25 |
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
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: 31 January 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
Three written assignments, up to a total of 60 pages (each written assignment is worth 15% , approx. 15 hours time committment) due early, mid and late semester
| During the teaching period | 45% |
Written examination
| During the examination period | 55% |
Last updated: 31 January 2024
Dates & times
- Semester 2
Principal coordinator Karim Seghouane Mode of delivery Dual-Delivery (Parkville) Contact hours 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 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Time commitment details
Estimated Total Time Commitment - 170 hours
Last updated: 31 January 2024
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science Course Master of Commerce (Actuarial Science) Course Master of Biostatistics Course Ph.D.- Engineering Course Graduate Diploma in Biostatistics Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Mathematics and Statistics) 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: 31 January 2024