Experimental Methods

Subject MAST90010 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 36 hours comprising 2 x one-hour lectures per week and 1 x one-hour workshop per week.
Total Time Commitment: Not available
Prerequisites: None
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements: It is University policy to take all reasonable steps to minimise the impact of disability upon academic study and reasonable steps will be made to enhance a student’s participation in the University’s programs. Students who feel their disability may impact upon their active and safe participation in a subject are encouraged to discuss this with the relevant subject coordinator and the Disability Liaison Unit.


Dr Nicole Bell


Email: n.bell@unimelb.edu.au
Subject Overview: This subject provides a full suite of tools for the sciences, in particular the experimental physical sciences. Five major areas will be covered: management of huge (petabyte) datasets, signal processing, advanced statistical methods, data mining and advanced experimental design. Where possible, the course will include specific case studies, drawn from the relevant disciplines. Examples are drawn from physics, mathematics, earth sciences and bioinformatics.

The objectives of this subject are:

  • to challenge the students to expand their knowledge of advanced experimental techniques;
  • to broaden their appreciation of new developments in areas such as grid computing;
  • to solve quantitative problems involving large experimental datasets;
  • to understand and implement standard signal processing algorithms in, for example, image analysis, time series analysis;
  • to understand the possible architectures to manage very large datasets;
  • to understand how one can distinguish a signal in the presence of large backgrounds through the use of data mining technologies;
  • Competency in Scientific Computer Programming.
Assessment: Three to five assignments totalling up to 50 pages of written work (50%), spaced evenly throughout the semester, plus a three-hour end-of-semester written examination (50%).
Prescribed Texts: Nil.
Recommended Texts: Nil.
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

At the completion of this subject, students should have gained skills in:

  • determining appropriate statistical techniques to apply to real-world problems and implement those techniques;
  • designing good experiments to solve well-posed problems;
  • applying abstract concepts to real-world situations;
  • solving relatively complicated problems using approximations;
  • participating as an effective member of a group in discussions and collaborative assignments;
  • managing time effectively in order to be prepared for group discussions and undertake the assignments and exam.
Notes: Students undertaking this subject will be expected to use computers; suitable access will be provided on campus.
Related Course(s): Master of Science (Physics)

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