This course has been adjusted for remote participation in Sem 2 2021, however students are encouraged to attend on-campus activities if possible.
The proper analysis of scientific data is the most powerful tool we have for separating scientific fact from fiction, and a key part of the process in the modern practice of science is getting the data into an electronic format. This class will provide an introduction to the electronics methods and techniques most useful in instrumentation and laboratory settings, along with an introduction to statistical and numerical techniques that are useful in the analysis and characterisation of data. Students will have the opportunity to learn and practice the methods of signal conditioning, analog to digital conversion (and the reverse), and low-noise circuit design that are key to the high-fidelity transformation of signals into data. When analysing data, a focus will be placed on conceptual understanding of how specific methods work and the situations in which they can and cannot be applied. A number of practical examples will be discussed during the course, providing the opportunity for hands-on learning through the processing of real data sets with statistical software and data evaluation programs. The experience gained in this course will help students approach their own research problems.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of this course, students will have the knowledge and skills to:- Understand of the principles of linear circuits, amplification and feedback, analog to digital conversion, digital to analog conversion, and the requirements of analog electronics to successfully interface with digital systems;
- Analyse, design, and build practical circuits;
- Interpret and evaluate electronic circuit diagrams;
- Apply the basic principles of the design of electronic circuits to optimise signals to noise ratios in data acquisition systems;
- Understand and perform a suite of statistical techniques;
- Evaluate data sets using appropriate techniques;
- Assess quality of data needed to obtain specific goals;
- Apply effectively a variety of data analysis tools.
Indicative Assessment
Assessment will be based on:- Theory exam (25% LO 5-7)
- Data Practicals (25% LO 5-8)
- Electronics Labs 25% (LO2-4)
- Oral reports 25% (LO1-3)
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Workload
Course includes a total of 30 hours of lectures and 40 hours of practicals. Students are expected to spend an additional 50 hours on work related to the course.Requisite and Incompatibility
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2021 | $4110 |
- International fee paying students
| Year | Fee |
|---|---|
| 2021 | $5880 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 7069 | 26 Jul 2021 | 02 Aug 2021 | 14 Sep 2021 | 29 Oct 2021 | In Person | N/A |
