Undergraduate Course: Programming, Data, Probability and Statistics (PHYS08061)
Course Outline
| School | School of Physics and Astronomy |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course introduces modern computer programming and data analysis based on a firm foundation of probability and statistics. It serves as preparation for further study in physics-related degree programmes (and is open to students on these degree programmes only). The course consists of lectures on the theoretical underpinning of probability and statistics and hands-on workshops to develop understanding, familiarity and fluency in their practical applications in computational and experimental physics by means of computer assistance. |
| Course description |
Programming
- Introduction to Python programming and Jupyter notebooks
- Data types, variables and operators
- File input and output
- Conditional statements, loops and lists
- Importing and using Python modules, mathematical functions, simple graphs
- Introduction to functions
- Reusable code, finding and fixing bugs
- Brief Introduction to Object Oriented Programming
Probability
- Discrete and continuous probabilities; connection to physical processes; combining probabilities; Bayes theorem
- Probability distributions and how they are characterised; moments and expectations; error analysis
- Permutations, combinations, and partitions; Binomial distribution; Poisson distribution
- The Normal or Gaussian distribution and its physical origin; convolution of probability distributions; Gaussian as a limiting form
- Waiting time distributions; resonance and the Lorentzian; power-law processes and distributions
Statistics and Data analysis
- Hypothesis testing; idea of test statistics; z-test; chi-squared statistic
- Parameter estimation; properties of estimators; maximum likelihood methods; weighted mean¿ and variance; minimum chi-squared method; confidence intervals
- Bayesian inference; priors and posteriors; maximum credibility method; credibility intervals
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
|
Quota: 30 |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 60,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
114 )
|
| Assessment (Further Info) |
Written Exam
35 %,
Coursework
65 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written exam - 35%
Coursework - 65%, made up of:
Checkpoint 1 - 5%
Checkpoint 2 - 5%
Checkpoint 3 - 5%
Checkpoint 4 - 10%
Checkpoint 5 - 15%
Checkpoint 6 - 25% |
| Feedback |
Offered throughout the course during workshop sessions, by staff and teaching assistants In workshops featuring a Checkpoint task, student will receive formative feedback during the workshop as their work is marked. (The marking process involves a discussion with a member of staff/teaching assistant). Feedback on all aspects to do with solving the checkpoint problem will be provided |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand how physical processes lead to probability distribution functions and models
- Use appropriate statistical methods to analyse data
- Construct an algorithm to solve a physical problem
- Write (using available packages and libraries) and debug code in Python for calculation and visualisation
- Think critically about the results of solving problems, and identify sources of error
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Kristel Torokoff
Tel: (0131 6)50 5270
Email: kristel.torokoff@ed.ac.uk |
Course secretary | Mrs Gillian MacDonald
Tel: (0131 6)51 7525
Email: gillian.macdonald@ed.ac.uk |
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