Studying online

There are now 3 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Units available in Online Restricted mode have been adapted for online study only for those students who require the unit to complete their studies and who are unable to attend campus owing to exceptional circumstances beyond their control. To be enrolled in a unit in Online Restricted mode, students should contact their Student Advising Office through askUWA

Click on an offering mode for more details.

Unit Overview

Description
This unit focuses on contemporary and advanced statistical learning methods in data science including unsupervised and supervised learning and dimension reduction for high-dimensional data. It combines concepts and practices and shows how to apply the methods to different data science domains (e.g. physical sciences, medical and biological sciences, engineering, business and social sciences) and how to critically assess each method. Combined with appropriate visualisation this will improve the understanding of the key ideas of each method and their applicability. Students will learn to choose suitable data analysis methods for particular data and discuss and interpret the results. Statistical computing (including R), as required in a position as a data scientist will form an essential part of this unit.
Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2OnlineOnline Restricted
Semester 2UWA (Perth)Face to face
Outcomes
Students are able to (1) evaluate concepts and methods of statistical data science and statistical learning; (2) compare supervised or unsupervised statistical methods in the analysis of particular data sets; (3) critically assess the suitability of different approaches; (4) analyse complex data sets as a result of developing code in a modern programming language; and (5) appraise the results of analysis of multivariate and high-dimensional data.
Assessment
Indicative assessments in this unit are as follows: (1) assignments; (2) laboratories and quizzes; and (3) final examination. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Professor Inge Koch
Unit rules
Prerequisites:
( Course Enrolment in the 62530 Master of Data Science ) and ( STAT2401 Analysis of Experiments and STAT2402 Analysis of Observations ) or STAT2062 Fundamentals of Probability with Applications
Co-requisites:
Nil
Incompatibility:
STAT3064 Statistical Learning and STAT4067 Applied Statistics and Data Visualisation
Contact hours
Lectures: 2-hours per week; Laboratory: 2-hours per week.
Note
STAT5061 will replace STAT4067 Applied Statistics and Visualisation from 2022.
Texts

This unit will be based on a number of recommended advanced texts including:

Analysis of Multivariate and High-Dimensional Data, Inge Koch

The Elements of Statistical Learning, Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Modern Multivariate Statistical Techniques, Alan J. Izenman

Statistical Data Science, Chapters 1-15, Inge Koch and A Pope

  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
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  • Unit readings, including any essential textbooks, are listed in the unit outline for each unit, one week prior the commencement of study. The unit outline will be available via the LMS and the UWA Handbook one week prior the commencement of study. Reading lists and essential textbooks are subject to change each semester. Information on essential textbooks will also be made available on the Essential Textbooks. This website is updated regularly in the lead up to semester so content may change. It is recommended that students purchase essential textbooks for convenience due to the frequency with which they will be required during the unit. A limited number of textbooks will be made available from the Library in print and will also be made available online wherever possible. Essential textbooks can be purchased from the commercial vendors to secure the best deal. The Student Guild can provide assistance on where to purchase books if required. Books can be purchased second hand at the Guild Secondhand bookshop (second floor, Guild Village), which is located on campus.