SCIENTIFIC INVESTIGATION SKILLS - 2023/4

Module Overview

This module covers a wide range of generic skills important in scientific investigation. These skills cover data handling, statistical analysis, Python programming skills, scientific writing, ethics (including academic misconduct), group working covering problem-solving, and public communication, plus library-based information research skills including information retrieval and referencing.

Module provider

Mathematics & Physics

Module Leader

FAUX David (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 4

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None  

Module content

Indicative content includes:

• Python Programming Skills


• Research Skills


• Probability: Discrete and continuous distributions, expectation values, Binomial, Gaussian and Poisson distributions.  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 resource 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, students work together in problem-solving activities and prepare joint reports.

Assessment pattern

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:

• Python programming skills


• ability to perform statistical analysis of data


• professional scientific communication skills

Thus, the summative assessment for this module consists of:

·         Data Handling assessments (weeks 1,3,5,7,9)

·         A short (500-800 word) essay (week 8) and peer review exercise (week 10)

·         Python programming consisting of three coursework exercises.

·         Team work activities consisting two problem-solving tasks (weeks 7 & 10)

 

Formative assessment and feedback

The first problem-solving exercise is formative.  It is marked and returned with feedback. All problem-solving exercises are supported by a 2-hour computer-based class during which students obtain feedback on progress.  For all problems, students see their marked reports with comments.  Students receive written feedback on their essay in the form of four peer-review forms. The computation part features formative exercises, with the debug-compilation-execution process providing instant feedback, with verbal feedback available from the supervisors in the session. The Data Handling activity includes formative online tests.

 

Module aims

• teach the basic elements of probability distributions and to be able to undertake simple statistical and error analysis.  To be able to use a computer spreadsheet to do such analysis, plot graphs and perform curve fitting.
• develop skills in the foundations of computational mathematics and Python programming
• develop skills in analysing 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. 
• to develop writing skills and referencing of scientific work through writing a short essay
• develop skills in critique and be able to participate in peer review
• to present scientific information in a form suitable for the general public
• to undertake a problem-solving activity as part of a team to produce collaborative reports.
• to develop skills in scientific modelling using a spreadsheet
• to present scientific information appropriately including use of diagrams, figures and graphs and presentation of equations and numbers

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• equip students with practical and professional skills


• provide students with subject knowledge and the ability to apply it to practical situations

The learning and teaching methods include:

• Two hours per week for the Python workshops


• Probability, Statistics, Data Analysis, Spreadsheets and Computer Algebra: A one hour lecture followed by a one hour tutorial session in a computing laboratory weekly, for six weeks.


• 6 hours of material delivered by University Library staff in a mixture of workshop and lecture format. 


• 19 hours of team working spread over the semester with sessions on essay work, scientific ethics, problem-solving support and presentation skills.

 

 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PHY1035

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.