Module code: PHY3054

Module provider


Module Leader


Number of Credits


ECT Credits



FHEQ Level 6

JACs code


Module cap (Maximum number of students)


Module Availability

Semester 1

Overall student workload

Lecture Hours: 11

Laboratory Hours: 22

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 50%
Examination FINAL EXAM 50%

Alternative Assessment


Prerequisites / Co-requisites

The module will assume prior knowledge equivalent to the following modules. If you have not taken these modules you should consult the module descriptors – Introduction to Astronomy (PHY2071)

Module overview

In this module, students will learn key methods adopted in astrophysics to carry out advanced research: scientific computing, statistics and data analysis. Much of the course develops highly transferrable skills that apply to science research in general. The goal is to ensure that students are well-prepared for either their research year or their future careers.

Module aims

Provide a clear perspective of how astrophysical research is conducted

Provide an introduction and hands-on experience of numerical tools used in scientific research

Learning outcomes

Attributes Developed
Design and construct programs and scripts in the modern and flexible Python language to perform tasks on real or simulated data KCPT
Understand basic numerical methods for astrophysical research like integration, differentiation and root finding KCPT
Visualise real or simulated data and prepare graphics and animations for presentations PT
Understand and apply key statistical concepts like Bayesian vs frequentist statistics, error analysis, fitting, comparison between data and models KCPT
Analyse and manipulate large data sets to extract physical properties KPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes:

Scientific computing:

a first program in python, reading and writing data, visualising data, scripting tasks, numerical integration, differentiation, root finding
simulation techniques  i.e. N-body simulations, Monte Carlo simulations


Bayesian vs frequentist statistics, likelihood functions, distributions and moments, fitting, comparing data and models, error analysis

Data analysis

Handling  large data files, extracting physical quantities of interest, image analysis, copying with noise and systematic errors.


Methods of Teaching / Learning

The learning and teaching strategy is designed to help students gain a basic understanding of the main research techniques used in astrophysics and prepare them for a research year or future career in science.


The learning and teaching methods include:

11 hours of lectures (1h/week)

22 hours of computational lab (2h/week)


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate understanding of the basic principles of python programming and scripting, statistics and data analysis, as detailed in the learning outcomes.


Thus, the summative assessment for this module consists of 1 piece of coursework and a final exam:

Coursework on scientific programming, scripting and simulation techniques (deadline week 8)

Final exam


For coursework the students will submit a report including a description of the problem and a critical discussion of the results obtained, and the original code developed for the task.


Formative assessment and feedback

Formative assessment consists of 1 piece of coursework (Coursework 0) on python programming and scripting (deadline week 5), for which the students will receive detailed feedback. Additional feedback will be provided during lab sessions by means of verbal feedback from the academics.


Reading list


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 2017/8 academic year.