- UCAS course code
- F305
- UCAS institution code
- M20
Master of Physics (MPhys)
MPhys Physics
Join a physics Department of international renown that offers great choice and flexibility, leading to master's qualification.
- Typical A-level offer: A*A*A including specific subjects
- Typical contextual A-level offer: A*AA including specific subjects
- Refugee/care-experienced offer: AAA including specific subjects
- Typical International Baccalaureate offer: 38 points overall with 7,7,6 at HL, including specific requirements
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £36,500 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.
For information about scholarships and bursaries please visit our undergraduate student finance pages and our Department funding pages .
Course unit details:
Introduction to Data Science
Unit code | PHYS10792 |
---|---|
Credit rating | 10 |
Unit level | Level 1 |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | No |
Overview
Introduction to Data Science
Aims
- To introduce basics of statistical methods and modern-day advanced data analysis techniques, as required in all fields working with data.
- To deepen the understanding of how data analysis works for small and large data samples.
- To obtain a comprehensive set of tools to analyse data.
Learning outcomes
This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Please see Blackboard / course unit related emails for any further updates
On completion successful students will be able to:
• Demonstrate an understanding of the basics of the statistical analysis of data.
• Explain methods of data analysis and their idea.
• Apply a set of analysis techniques as required for basic and advanced datasets.
• Critically assess new results derived from datasets.
• Use the knowledge of statistical data analysis to understand more advanced and new techniques.
Syllabus
• Probabilities and interpretations
• Probability distributions
• Parameter Estimation
• Maximum Likelihood and extended maximum likelihood
• Least Square, chi2, correlations
• Monte Carlo basics
• Probability and confidence level
• Hypothesis testing
• Goodness of fit tests
• Limit setting
• Introduction to Multivariate Analysis Techniques
Assessment methods
Method | Weight |
---|---|
Other | 10% |
Written exam | 90% |
* Other = Online quizzes.
Feedback methods
Feedback is through exercises (via online feedback) and the exam.
Recommended reading
Barlow, R., Statistics – A Guide to the Use of Statistical Methods in the Physical Sciences, Wiley
Cowan, G., Statistical Data Analysis, Oxford
Behnke, O., et al, Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods, Wiley
Study hours
Scheduled activity hours | |
---|---|
Assessment written exam | 1.5 |
Lectures | 24 |
Independent study hours | |
---|---|
Independent study | 74.5 |
Teaching staff
Staff member | Role |
---|---|
Andrew Markwick | Unit coordinator |
Michaela Queitsch-Maitland | Unit coordinator |