Studying online
There are now 2 possible online modes for units:
Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.
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) Availability Location Mode Semester 2 UWA (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)
- Dr Darfiana Nur
- Unit rules
- 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.
- All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
- 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.
- Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.
Face to face
Predominantly face-to-face. On campus attendance required to complete this unit. May have accompanying resources online.
Online flexible
100% Online Unit. NO campus face-to-face attendance is required to complete this unit. All study requirements are online only. Unit is asynchronous delivery, with NO requirement for students to participate online at specific times.
Online timetabled
100% Online Unit. NO campus face-to-face attendance is required to complete this unit. All study requirements are online only. Unit includes some synchronous components, with a requirement for students to participate online at specific times.
Online Restricted
Not available for self-enrolment. Students access this mode by contacting their student office through AskUWA. 100% Online Unit.
NO campus face-to-face attendance. All study and assessment requirements are online only. Unit includes some timetabled activities, with a requirement for students to participate online at specific times. In exceptional cases (noted in the Handbook) students may be required to participate in face-to-face laboratory classes when a return to UWA’s Crawley campus becomes possible in order to be awarded a final grade.
External
No attendance or regular contact is required, and all study requirements are completed either via correspondence and/or online submission.
Off-campus
Regular attendance is not required, but student attends the institution face to face on an agreed schedule for purposes of supervision and/or instruction.
Multi-mode
Multiple modes of delivery. Unit includes a mix of online and on-campus study requirements. On campus attendance for some activities is required to complete this unit.