Program Overview
Introduction to the Scientific Investigation Skills Module
The Scientific Investigation Skills module is designed to equip students with a wide range of generic skills essential in scientific investigation. These skills encompass data handling, statistical analysis, Python programming, scientific writing, ethics, group working, problem-solving, and public communication, as well as library-based information research skills.
Module Overview
This module covers the basic elements of probability distributions, statistical and error analysis, and the use of computer spreadsheets for analysis, graph plotting, and curve fitting. It aims to develop skills in the foundations of computational mathematics and Python programming, data analysis, and issues of ethics in science and academic misconduct.
Module Details
- Module Code: PHY1035
- Module Provider: Mathematics & Physics
- Module Leader: HENDERSON Jack (Maths & Phys)
- Number of Credits: 15
- ECTS Credits: 7.5
- Framework: FHEQ Level 4
- Module Cap: N/A
Student Workload
The overall student workload is distributed as follows:
- Workshop Hours: 46
- Independent Learning Hours: 31
- Lecture Hours: 6
- Tutorial Hours: 6
- Guided Learning: 55
- Captured Content: 6
Module Availability and Prerequisites
- Semester: 1
- Prerequisites/Co-requisites: None
Module Content
The module content includes:
- Python Programming Skills
- Research Skills
- Probability: Discrete and continuous distributions, expectation values, Binomial, Gaussian, and Poisson distributions, and the Central Limit Theorem
- Statistics: Mean, standard deviation, standard error in mean
- Data Analysis: Propagation of errors, least-squares fitting; c2-distribution
- Spreadsheets: Excel spreadsheets including calculations, numerical simulation, and graphs
- Ethics: Ethical scientific conduct, issues of plagiarism, and proper referencing in science
- Using the University Library, including the different types of resources available, how to search the library catalogue, understanding different types of citations, appropriate referencing, and searching for authoritative information on the web
- Communicating scientific work appropriately for the relevant audience through writing and via press statements
- Team working, where students work together in problem-solving activities and prepare joint reports
Assessment Pattern
The assessment pattern for this module is as follows: | Assessment Type | Unit of Assessment | Weighting | | --- | --- | --- | | Coursework | Essay | 20 | | Coursework | Team Work | 30 | | Coursework | Data Handling | 20 | | Coursework | Computational Exercises | 30 |
Alternative Assessment
An individual piece of work equivalent to an individual's contribution to a Team Work exercise can be arranged.
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate their skills in Python programming, statistical analysis of data, and professional scientific communication.
Module Aims
The module aims to:
- Teach the basic elements of probability distributions and simple statistical and error analysis
- Develop skills in the foundations of computational mathematics and Python programming
- Develop skills in analyzing data
- Explore issues of ethics in science and academic misconduct
- Provide an introduction to finding suitable information from different sources available through the library and referencing the sources appropriately
- Develop writing skills and referencing of scientific work through writing a short essay
- Develop skills in critique and participate in peer review
- Present scientific information in a form suitable for the general public
- Undertake a problem-solving activity as part of a team to produce collaborative reports
- Develop skills in scientific modeling using a spreadsheet
- Present scientific information appropriately, including the use of diagrams, figures, and graphs, and the presentation of equations and numbers
Learning Outcomes
The learning outcomes for this module are detailed in the table below: | | Attributes Developed | | --- | --- | | 001 | Analyze and present reduced experimental and probabilistic results of multiple measurements of physical observables | C | | 002 | Quote averages and errors of such variables | K | | 003 | Fit theoretical predictions to graphs where one independent observable is changing using the method of least squares, and find the errors in the fitting parameter(s) | C | | 004 | Use simple error theory to find the errors of quantities dependent on (combinations of) the observables | C | | 005 | Use simple probability distributions to predict the outcome of experiments | C | | 006 | Take simple mathematical problems and write Python programs which correctly implement the mathematics, using correct syntax to give a working problem which the student will be able to debug, compile, and run | CPT | | 007 | Use Python to generate well-presented numerical and graphical output | CPT | | 008 | Be aware of and understand how to access library resources available in the University Library and online | T | | 009 | Understand different types of citations, including those for books and journals | P | | 010 | Use the web for authoritative information | T | | 011 | Be able to find information from different sources available in the University Library | PT | | 012 | Be able to write bibliographies in a variety of formats and reference appropriate sources | PT | | 013 | Understand the structure used in different types of scientific writings | T | | 014 | Understand the principle of peer review and be able to critically review work | P | | 015 | Work collaboratively as a team member to solve problems and formulate joint reports | PT | | 016 | Present science in a manner suitable for consumption by the general public | KPT | | 017 | Undertake individual research on a science topic and present in essay format | KCPT | | 018 | Produce a simple numerical simulation using EXCEL | PT | | 019 | Use EXCEL for graphical presentation of data | PT |
Attributes Developed
- C: Cognitive/analytical
- K: Subject knowledge
- T: Transferable skills
- P: Professional/Practical skills
Methods of Teaching and Learning
The learning and teaching strategy is designed to equip students with practical and professional skills and provide them with subject knowledge and the ability to apply it to practical situations. The methods include Python workshops, lectures followed by tutorial sessions in a computing laboratory, material delivered by University Library staff, and team working activities.
Programmes this Module Appears In
This module appears in the following programmes:
- Physics BSc (Hons)
- Physics MPhys
- Physics with Astronomy BSc (Hons)
- Physics with Astronomy MPhys
- Physics with Nuclear Astrophysics BSc (Hons)
- Physics with Nuclear Astrophysics MPhys
- Physics with Quantum Computing BSc (Hons)
- Physics with Quantum Computing MPhys
Qualifying Conditions
A weighted aggregate mark of 40% is required to pass the module for all programmes.