Physics Skills - Programming and Data Analysis

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Write code to analyse and present experimental/observational data

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Use existing software libraries in your own code
• Design, run, debug and test computer programs
• Analyse problems in a systematic manner and develop algorithms to solve them computationally

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Interpret experimental/observational results correctly

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Fundamental statistical concepts, including probability distribution functions, cumulative distribution functions, hypothesis testing, parameter estimation and model fitting
• Basic programming constructs, including sequence, selection and iteration, the use of identifiers, variables and expressions, and a range of data types
• Good programming style

Programming

• Writing and running programs
• Variables and data types
• Basic control flow: looping, branching and function calls
• Functional programming
• Computational thinking
• Python libraries (for mathematics, data analysis and display)
• Designing algorithms (Moving from problem to solution)

Statistics/Data Analysis

• Describing data
• Defining & understanding probability
• Probility distribution functions and cumulative distribution functions
• Statistical distributions: Gaussian, Binomial, Poisson, Chi-squared
• The central limit theorem
• Understanding uncertainty -- statistical and systematic errors
• Testing for and understanding correlations
• Hypothesis testing
• Understanding statistical significance
• Model fitting and parameter estimation via least-squares and Chi-squared

The aim of this module is to give students practical skills, so the teaching and learning methods used are designed to accomplish this. Formal lectures will be used primarily to introduce key ideas and concepts, but even these will be illustrated with extensive practical/computational examples and visualisations. Most of the teaching will take place during extended "practical" sessions, during which students will be expected to carry out programming and data analysis tasks that are related to -- and illustrative of -- the concepts that are being explored in the module at that time. Ideally, the formal lecture content will take place immediately before or during these sessions, so that new theoretical concepts being introduced can immediately be explored in practice by students. Teaching support in the form of multiple demonstrators will be available during all sessions, so that one-on-one help is available as needed. Additional learning is expected to take place independently, again mostly in the form of practical programming and data analysis. Lecture notes and practical examples will be made available in the form of ipython notebooks.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.