Master of Physics (MPhys)

MPhys Physics

Join a physics Department of international renown that offers great choice and flexibility, leading to master's qualification.

  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: F305 / Institution code: M20
  • Key features:
  • Scholarships available
  • Accredited course

Full entry requirementsHow to apply

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

Course unit fact file
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

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