STH415 Advances in Experimental Physics
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | STH415 |
|---|---|
| Prerequisite(s): | Admission to ST10 or (Admission to ST20 and completion of 288 credit points (CP)) |
| Equivalent(s): | STB415 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 2 2027, Gardens Point, Internal
| Unit code: | STH415 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to ST10 or (Admission to ST20 and completion of 288 credit points (CP)) |
| Equivalent: | STB415 |
Overview
This Honours level unit develops advanced knowledge in experimental physics. It provides an essential platform for further study and for theoretical and experimental research into the physical world, from subatomic particles to the universe, with a strong emphasis on statistical approaches to data analysis.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply experimental and computational approaches to solve real-world problems in physics.
- Apply statistical and computational methods, including multivariate analysis, model fitting and model selection, to analyse and interpret data.
- Acquire, preprocess and analyse research- or industry-provided data, and communicate findings through appropriate visualisations.
- Communicate analytical approaches and results to professional audiences, and evaluate their application to a real-world problem.
Content
Content is delivered through weekly lectures and tutorials covering the advanced theory and practice of experimental data analysis, with a strong emphasis on statistical methods. Lectures introduce the techniques; tutorials work through homework and analysis problems that build toward the assessment. Topics vary from year to year to suit the cohort and may include (but are not limited to): advanced techniques in data acquisition and preprocessing; comprehensive data analysis, including multivariate methods; data fitting and model selection techniques; data visualisation techniques.
Learning Approaches
This unit is delivered through weekly two-hour lectures supported by weekly tutorials. Lectures cover advanced experimental and statistical data-analysis techniques, and tutorials work through homework and analysis problems using computational approaches. You will also be expected to devote considerable time to individual study, including working with recommended resources, solving practice problems, completing tutorial problem sets, and reading selected research papers. Feedback is provided continuously throughout the semester, and comprehensive lecture notes are available on Canvas.
Feedback on Learning and Assessment
Feedback is provided through tutorial activities where data-analysis techniques are applied, through written feedback on the problem-solving tasks, and through the oral examination.
Assessment
Overview
Assessment in this unit is built around a work-integrated project in which you address a real-world problem using data supplied by an industry partner. Two problem-solving tasks (20% at mid-semester, 40% towards the end of semester) take you from acquiring, cleaning and scoping the industry-provided data through to a full analysis using multivariate methods, model selection and clear visualisation, producing an analysis of the kind expected in a research or industry setting. An end-of-semester oral presentation (40%) then asks you to report your findings to a professional audience that may include the industry partner, replicating the professional practice of communicating analytical work back to a client.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task 1
A problem-solving task in which you begin work on a real-world problem using data supplied by an industry partner. Working as a practising data analyst would, you acquire, clean and preprocess the provided dataset, carry out initial exploratory analysis, and scope an appropriate analytical approach to the partner's problem.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Problem Solving Task 2
A problem-solving task in which you complete a full analysis of the industry-provided data to address the partner's real-world problem. You select and justify appropriate multivariate methods, fit models and perform model selection, quantify uncertainty, and communicate your results through clear visualisations and a concise analysis report. This is an authentic task where you make use of imperfect data supplied by an industry partner and apply professional judgement to choose, apply and defend an analysis workflow that solves a genuine problem, rather than follow a prescribed procedure.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: Oral Examination
An oral presentation in which you report the findings of your work-integrated analysis, communicating your approach and results to a professional audience that may include the industry partner who supplied the data. The task replicates the authentic professional practice of reporting analytical work on a real-world problem back to a client or stakeholder. It requires professional judgement to convey technical methods and results clearly, justify your analytical choices, and respond to questions, producing the kind of stakeholder presentation an experimental physicist or data analyst delivers in practice.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There is no prescribed text for this unit. You will be directed to a range of resource material that will be related to your specific area of study.
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.ST10 Bachelor of Science (Honours)
- Demonstrate an advanced level of knowledge in one or more discipline area(s) and knowledge of research methods, principles and practices.
Relates to: ULO1, ULO2, ULO3, ULO4, Problem Solving Task 1, Problem Solving Task 2 - Solve complex problems by demonstrating and applying advanced knowledge and skills in scientific principles, concepts and techniques, with scientific rigour
Relates to: ULO2, Problem Solving Task 1, Problem Solving Task 2 - Demonstrate professional reflective practices and accountability in scholarship and the research process including awareness of ethical and cultural responsibility.
Relates to: ULO4, Problem Solving Task 2
ST20 Bachelor of Science Advanced (Honours)
- Articulate advanced and coherent knowledge and understanding of at least one scientific discipline
Relates to: Problem Solving Task 1, Problem Solving Task 2 - Critically evaluate and apply advanced scientific principles to design experiments, and collect, record, analyse and interpret data using appropriate scientific methodologies, and critical thinking, judgment and deduction to solve complex problems
Relates to: Problem Solving Task 1, Problem Solving Task 2 - Evaluate and synthesise complex scientific information, research outcomes and arguments and communicate these to scientists and non-scientists using appropriate communication styles in a variety of formats.
Relates to: Problem Solving Task 2