MSc Health Data Science

For entry in the academic year beginning September 2026, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £15,800
  International, including EU, students (per annum): £35,700
• PGDip (full-time)
  UK students (per annum): £12,600
  International, including EU, students (per annum): £28,600
• PGDip (part-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (full-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (part-time)
  UK students (per annum): £3,150
  International, including EU, students (per annum): £7,150

Further information for EU students can be found on our dedicated EU page.

The course fees include all the tuition, technical support and examinations required for the course. All fees for entry will be subject to yearly review. Courses lasting more than one year may be subject to incremental rises per annum. For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

The University permits applicants with comparable previous experience to submit an application for consideration of AP(E)L Accreditation Prior (Experiential) Learning. The maximum AP(E)L is 15 credits to a PGCert, 45 credits to a PGDip and 60 credits to a MSc.

If your AP(E)L application is successful, the University charges £30 for every 15 credits of AP(E)L. The overall tuition fee is adjusted and then the administrative charge is applied.

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).

For the latest scholarship and bursary information please visit the fees and funding page .

The Catherine Chisholm scholarship is applicable to students from selected countries for this course. Find out more details on the scholarship page .

The University of Manchester is proud to offer six fully-funded scholarships to Women from Brunei, Cambodia, Indonesia, Lao PDR, Myanmar, the Philippines, Singapore, Thailand or Timor-Leste completing specific master's courses in STEM subjects. Please visit the STEM scholarship page for more information.

• Fundamental data types and structures
• Core programming concepts such as iteration, selection, file handling and control flow
• Develop awareness of programming languages used in Data Science (e.g. Python, R, Julia)
• The fundamentals of using a modern programming language (e.g. Python) for data science and associated commonly used libraries/modules
• How data is stored and accessed using database systems
• Data manipulation and pre-processing (cleaning, imputation, encoding and transforming data)
• Combining datasets (data linkage)

The unit aims to:

• Facilitate the practice of writing code scripts in a modern programming language suitable for data science tasks, while applying best software engineering practices & standards
• Give learners experience of manipulating data presented and stored in different formats (e.g. JSON, XML, CSV)
• Build familiarity with using, accessing and querying data in different database storage systems (e.g. relational database systems)
• Understand and practice data wrangling, transformation and cleaning strategies to make data usable for analytic purposes
• Produce data visualisations to explore and present data
• To develop 'algorithmic thinking' and problem solving strategies

The unit is delivered in a blended format. Self-directed learning material is delivered through interactive digital (Jupyter) notebooks to impart core knowledge and skills with weekly online synchronous sessions allowing learners to work in teams to solve and practice coding problems. This is further supported by two face-to-face hackathon events allowing learners to load, combine and visualise data in the first hackathon. Building on this in the second hackathon, learners apply methods to clean and process data in order to carry out subsequent analysis.

Upon completion, students should be able to: 

LO1: Demonstrate a critical understanding of the fundamental principles and concepts of programming (e.g. iteration, selection, control flow and data representation using data-frames) using a modern programming language for data science (e.g. Python) 

LO2: Identify and explain key modules essential for health data science (e.g. NumPy, Pandas, re, and Matplotlib), with a focus on data-frame manipulation 

LO3: Describe basic statistical concepts and their application in health data analysis, and how they are represented/used within a programming language

Upon completion, students should be able to: 

LO4: Apply 'algorithmic thinking' to solve problems using programming concepts (e.g. selection, iteration, functions, etc.) 

LO5: Analyse and manipulate health-related datasets using data-frames, including indexing, filtering, aggregation, and integration with SQL databases 

LO6: Interpret and visualise data effectively for insights and decision-making in health-related contexts, using common visualisation libraries

Upon completion, students should be able to: 

LO7: Write code for basic statistical analysis and data visualisation tasks, incorporating data-frame operations 

LO8: Clean and pre-process health-related datasets using regular expressions and data-frame methods 

LO9: Write and execute queries in SQL and integrate with code 

LO10: Implement best practices in coding to structure and document code (e.g. use of data structures, functions, classes, comments, code standards such as PEP8)

Upon completion, students should be able to: 

LO11: Experience 'team science' to solve problems collaboratively 

LO12: Develop an analytical problem solving mind-set

Feedback will be provided via Canva 15 working days after submission.

• Dawson, M (2010) Python Programming 3rd Ed. Australia: Course Technology PTR
• Molin, S (2019) Hands-On Data Analysis with Pandas. Birmingham: Packt
• Medium (2024) Towards data science: A Medium publication sharing concepts, ideas and codes. https://towardsdatascience.com/about 
• NHS (2024) NHS Python Community. https://nhs-pycom.net/