| Unit name | Practical Physics II: Laboratory and Computational Skills |
|---|---|
| Unit code | PHYS20037 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Dr. Basiri Esfahani |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) | |
| Units you must take alongside this one (co-requisite units) |
N/A |
| Units you may not take alongside this one |
SCIF20002, PHYS20038 Practical Physics II: Investigative Skills, PHYS20036 Practical Physics II: Laboratory and Data Science |
| School/department | School of Physics |
| Faculty | Faculty of Science |
Why is this unit important?
Physics is a practical subject and the laboratory curriculum is a key component of your training. You have had an introduction to laboratory practice and procedures in your first year; we now extend your learning into more independent laboratory-based learning through investigative experiments, while enhancing your confidence in analysis and in presenting your findings.
Alongside your laboratory learning, computing is an essential skill in all aspects of our society; however in quantitative science it is an indispensable aid to the work which we do. This unit will advance your skills in computational physics, applying appropriate numerical techniques to solving problems in physics, and giving you experience in good programming practice, software testing and version control.
How does this unit fit into your programme of study
This unit follows on from your first year Practical Physics unit; it continues your investigations of physical phenomena in a practical environment, further honing your laboratory skills and your skills in using computational skill to build physical models. You will develop increasing independence in the laboratory and in developing computing tasks to analyse complex data sets.
An overview of content
You will undertake a series of experiments over an extended period of time to investigate phenomena across a number of areas of physics, including (but not limited to) astrophysics, waves and optics, quantum phenomena, properties of matter and electronics. You will also use the coding principles encountered in first year and build on these to develop your understanding of numerical techniques in a physical context. The following computing approaches will be covered:
All of which will be applied to:
Numerical approaches for solving physical problems governed by sets of differential equations.
How will students, personally, be different as a result of the unit
While you will not undertake every experiment available in the laboratory, you will directly undertake a number of experiments across the themes listed above, giving you an appreciation of the importance of practical measurement in physics. A more advanced understanding of a coding language such as Python will allow you to more readily adapt to using other languages, and you will start to apply “computational thinking” to write code no matter the language needed. Through introduction of more systematic coding and debugging techniques you will write code which is easier to understand, debug and maintain.
Learning outcomes
By the end of this unit, you will:
As with your first year curriculum, you will predominately learn through carrying out a number of experiments; however in this second year, you will be assigned to fewer experiments, but for a longer timeframe. You will typically have between 18 and 24 hours of laboratory time over three weeks to complete your experiment. This additional time allows you to analyse data as well as to carry out any reading around the area and to discuss with academics to help you generate ideas for how to progress the investigation. The investigation will be carried out in supervised laboratory sessions, and you will have access to asynchronous online materials and to experts in the laboratory who can help you locate information and resources to support your experimentation.
You will learn the computational techniques through a combination of:
You will also be introduced to the appropriate use of online knowledge bases for the purposes of developing and refining your code.
Tasks which help you learn and prepare for summative tasks
You will have many opportunities for feedback in the laboratory; discussions with demonstrators in the laboratory, with staff experts, and from the technical staff in the laboratory. This will help you refine your experiments and optimise your results. Additionally, you can request feedback at any time on your experimental practice or in your experimental record keeping (lab note book). Through the duration of the computing course you will be able to attempt the problems in the Jupyter notebooks and gain verbal feedback on them during the drop-in sessions. This feedback, when used appropriately, will help you to develop your responses for the summative exercises.
Additionally, appropriate use of feedback received on summative tasks will help you in future tasks.
Tasks which count towards your unit mark
The summative assessment in the laboratory will come from:
When assessment does not go to plan
This unit is 100% coursework and does not have a resit opportunity except in a supplementary year; it is therefore expected that you engage with the content throughout and submit your work in a timely manner. If you are unable to engage it is important you discuss with your tutor early so that appropriate mitigation strategies may be identified and implemented quickly.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. PHYS20037).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
