Description

The practical laboratory work section of this module provides a broad foundation in experimental physics, upon which experimental work in Stage 2 and project work in Stage 3 builds. It starts with a short series of lectures, supplemented with a problems set, on error analysis and graph plotting. Laboratory work is normally undertaken in pairs, with support from demonstrators. Experiments are recorded in lab-books and presented as formal reports. The last of the experiments is done as a larger group and involves joint work on a poster, presented in a conference-like environment, and a formal report.

In the IT Skills section of this module students learn to use Python for scientific applications. Python is an interpreted, high-level, general-purpose programming language that can be used for a range of academic and research-based activities including high level mathematics and data processing work. Python is widely used in commercial and research environments.

The PHY0000 Communication and Key Skills course constitutes the third section of this module.

Module Aims

Experimentation is one of the central activities of a scientist. Experimental observations form the bases for new hypotheses and test scientific theories. In this module, you will learn to understand and to apply the experimental method, develop your ability to make reliable measurements and to report them in an effective and ethical manner, and write clearly structured and documented programs in Python.

Intended Learning Outcomes (ILOs)

A student who has passed this module should be able to:

• Module Specific Skills and Knowledge:
  1. use a computer language (i.e. Python) to manipulate data and solve equations using numerical methods;
  2. plan and execute experimental investigations;
  3. apply and describe a variety of experimental techniques;
  4. identify, estimate, combine and quote experimental errors and uncertainties;
• Discipline Specific Skills and Knowledge:
  5. keep accurate and thorough records;
  6. discuss and analyse critically results of investigations;
  7. minimize experimental errors and uncertainties;
  8. demonstrate awareness of the importance of safety within the laboratory context and of the relevant legislation and regulations;
  9. identify the hazards associated with specific experimental apparatus, and comply with the safety precautions required;
  10. deliver written reports and poster presentations;
  11. work in a team (working in pairs on standard experiments and in groups of four or more for extended experiments and poster presentations);
  12. manage time (meeting deadlines for assignments);
  13. use computers for data analysis and collection;
  14. collect, analyse and report data and conclusions in an ethical manner;
• Personal and Key Transferable / Employment Skills and Knowledge:
  15. use a computer to solve problems;
  16. solve problems logically;
  17. interact with demonstrators in a laboratory environment;
  18. as specified in PHY0000 Communication and Key Skills component.

Syllabus Plan

Part A: Practical Laboratory

General guidance on the module, experiments, data analysis and result reporting is provided in the Laboratory Manual. Each experiment is described in a laboratory script.

General supervision and assistance are available from the demonstrators during the timetabled practical sessions. Each demonstrator conducts the initial discussion and monitors the progress of the assigned students, taking a pastoral role and reporting any problems to the module leader. Feedback is given during an assessment and feedback discussion with a demonstrator. Poster presentations in the Student Conference are assessed by demonstrators.

Note: The Communication and Key Skills content and activities are described in the PHY0000 component description.

Part B: IT Skills

1. Introduction to Python
   1. Running interactive Python; loading modules and packages; using Python as a graphical calculator; simple calculations, maths, simple functions and plotting.
   2. Using Jupyter notebooks with Numpy and Matplotlib.
2. Core Python programming
   1. Objects, variables and assignments. Dynamic 'Duck' typing. Numerical datatypes.
   2. More datatypes: strings, lists, tuples, and dictionaries.
   3. Control flow I: Conditionals, comparisons and Boolean logic.
   4. Control flow II: Loops.
   5. Functions: keyword and positional arguments, default arguments, *args and **kwargs, docstrings, variable scope.
   6. Program structure and documentation, error handling, testing and debugging.
3. Python for labs
   1. Numpy arrays and datatypes.
   2. Using Numpy for reading and writing data; simple statistics; plotting data with error bars.
   3. Fitting a straight line with a least-squares fit.
   4. Nonlinear least-squares fitting with Scipy.
   5. Publication-quality plots with Matplotlib: multiple axes, control of plot elements.

Learning and Teaching

Learning Activities and Teaching Methods

Assessment

Notes: Refer to the Undergraduate Handbook for details and marking criteria. Briefly, lab- books and reports for standard experiments are marked in a one-to-one assessment and feedback discussion, involving a demonstrator; reports for the extended experiment are marked by two demonstrators, without final discussions; marking of poster presentations involves multiple demonstrators and may be moderated via peer assessment.

Resources

The following list is offered as an indication of the type & level of information that students are expected to consult. Further guidance will be provided by the Module Instructor(s).

Core text:

• Not applicable

Supplementary texts:

• Young H.D. and Freedman R.A. (2015), University Physics with Modern Physics (14^th edition), Pearson, ISBN 978-1-292-10031-9

ELE:

• http://newton.ex.ac.uk/redirects/ELE/phy1030

Further Information

Prior Knowledge Requirements

Re-assessment

Re-assessment is not available except when required by referral or deferral.

Notes: Re-assessment is not available for this module.

KIS Data Summary

Miscellaneous