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


This unit introduces fundamental concepts and contemporary methods of Statistical Learning – both supervised and unsupervised learning approaches, and shows how to apply these methods to different data science domains (e.g. physical sciences, medical and biological sciences, engineering, business and social sciences). Focus will be on the interaction between methods and data, on learning to choose suitable methods of data analysis for particular data and on interpreting the results. Statistical computing (including R and/or Matlab) will form an essential part of this unit.

6 points
(see Timetable)
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Level 3 core unit in the Data Science; Computing and Data Science; Human Sciences and Data Analytics major sequences
  • Level 3 option in the Mathematics; Mathematics; Statistics major sequences

Students are able to (1) explain the basic concepts and methods of Statistical Learning; (2) choose appropriate supervised or unsupervised approaches for a particular data set; (3) critically assess the suitability of the approach for a particular data set; (4) use modern programming languages to analyse data; (5) interpret results of multivariate data analysis; and (6) communicate results of multivariate data analysis..


Indicative assessments in this unit are as follows: (1) assignment; (2) in-semester test; and (3) exam. 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
Enrolment in
MJD-CDSDM Computing and Data Science
or MJD-MTHST Mathematics and Statistics
or MJD-DATSC Data Science
or MJD-HSDEM Human Sciences and Data Analytics
STAT2401 Analysis of Experiments
and STAT2402 Analysis of Observations
STAT2062 Fundamentals of Probability with Applications
or STAT2063 Probabilistic Methods and their Applications
STAT4067 Applied Statistics and Data Visualisation
and STAT5061 Statistical Data Science
Advisable prior study
CITS2402 Introduction to Data Science
Contact hours
Lectures: 2-hours per week
Laboratory: 2-hours per week
  • 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.