Online course
Data Analytics and Social Statistics
- Qualification: MSc, PGDip
- Duration: 18-27 months, part-time
- Workload: Approx 20 hours per week
- Next enrolment: September 2024
Introduction
Learn to master social data
The field of data analytics is developing rapidly. With the rise of ever larger and more specialised datasets, it’s essential to understand how to collect, handle, evaluate and interpret data to unleash its true potential.
Through studying this fully online, part-time course, you will learn to process and analyse complex social data effectively, improving your skills and professional outcomes in the process.
Leveraging real-world data and R software, this practical course will ensure you learn applicable techniques to take into the workplace.
Key features
Applied and practical learning
Use of real-world data and free R software to match day-to-day work scenarios.
Research and teaching excellence
Trusted by worldwide students and supported by high profile academics.
Latest methods and techniques
Interdisciplinary approach covering latest methods such as machine learning.
Watch Majid's story
Being able to produce statistics with data visualisation and to communicate this data to a range of different audiences are skills that are highly transferable to a range of exciting industries.
Majid, Social Researcher for HMRC
Real-world data and interdisciplinary expertise
- Learn the tools and hone the techniques you will use in day-to-day data analysis work
- Practically apply your learning to current and developing trends and topics across disciplines
- Work closely with course colleagues from diverse professional and global backgrounds
Key information
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Delivery
100% online learning
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Qualification
MSc (180 credits) - to achieve a Master of Science, you need to complete six 20-credit units, a mandatory 20-credit RSiP unit, and a 40-credit dissertation project
PGDip (120 credits) - to achieve a PG Diploma, you need to complete six 20-credit units -
Duration
MSc - 27 months, part-time
PGDip - 18 months, part-time -
Enrolment date
September
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How to apply
Find out how to apply and what documents to submit in the application and selection section
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Workload
Approx 20 hours per week
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Course director
Dr Alexandru Cernat, Senior Lecturer in the Department of Social Statistics and the chair of the Social Statistics Section of the Royal Statistical Society
Fees and funding
Total course tuition fees for entry in September 2024 are:
- MSc - £16,000 (UK/EU/International)
- PGDip - £10,666 (UK/EU/International)
You can save up to 15% on your tuition fees. Please see our fees and funding section below for more details.
We offer payment by instalments , so you can spread the cost of studying with us.
Explore a range of scholarships and bursaries available for this course below.
Entry requirements
An Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline.
We may also consider exceptional applicants with a Lower Second (2:2) class honours degree in a social science discipline (or the overseas equivalent), with either research experience or equivalent professional background.
Watch Andrea's story
One of the skills I gained from my degree is being able to explain complex data to different audiences and with different levels of understanding, which is a valuable skill in both academia and industry.
Andrea Lisette Aparicio Castro,
PhD student at The University of Manchester
Contact us today
Course overview
Who this course is for
If you are interested in upskilling and discovering the power of data for predicting trends and improving outcomes, this course is ideal. Data Analytics and Social Statistics is designed for any professional working in an industry which uses big data and social data. It is multidisciplinary, using methods that are applicable and relevant in diverse fields, from education, health, and business analytics, to public, private and non-profit sectors such as charities and NGOs.
Whether you have a background in data analytics or are looking for a big data course to gain this knowledge, this course offers a thorough grounding in this exciting field. Incorporating data collection, analysis, and presentation, with acknowledgement of big data and machine learning, this course will ensure you are at the forefront of developments in social data analysis.
This course is suitable for both working professionals who already work in this field and those who wish to change careers. Extensive experience in big data or heavy mathematics skills are not required. If you do not have professional experience in data analytics but have a strong background in social sciences, you can use this course as a conversion to transition into a new vocation in this dynamic field.
What you will learn
Through Data Analytics and Social Statistics, you will learn the practical, applied knowledge to empower you to unleash the true value of data. Throughout this course, you will learn to carry out advanced statistical modelling and create dynamic data visualisations to show new insights. You will create and manage datasets of various sizes, boosting your skills and realising the true potential of these valuable data.
You will also understand the key concepts of uncertainty and randomness in scientific writing. This course will teach you to exhibit a critical awareness of operationalisation and measurement issues in the social sciences. You will gain strong academic writing ability in the social sciences, using independent thinking to express research using data analytics.
How it will benefit your career
Professionals who can process and interpret rich data are in high demand across many different industries such as public policy, market research, education, non-profit organisations and more. Many of the significant policy challenges of our time are global, from food insecurity, war, disease and public health, and climate change. Big data plays an increasingly important role in helping social scientists understand and address these issues. Analysis of big data has the potential to reveal patterns that are not so easily understood or readily observable, leading to more robust strategies and responses.
Through studying Data Analytics and Social Statistics, you will develop the skills needed to advance in this field and drive your career forward. Learning to leverage data, you will be able to spot and predict trends and understand social behaviour more accurately. If you are looking to change careers, this course will give you a thorough grounding in big data analysis to empower you to make that move.
Where and when you will study
This course is 100% online, allowing you to study with The University of Manchester from anywhere in the world. You can learn flexibly at a time and pace that suits you. You will gain access to the University’s quality teaching, benefiting from the expertise and reputation of our School of Social Sciences, ranked 5th in the UK (The Times Higher Education Guide 2022).
All course material is available through the virtual learning environment (VLE) and includes video, assessments, workbooks and more. You will also benefit from interactive teaching and the chance to collaborate with your course peers from your global community.
Course units
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1. Data Cleaning and Visualisation Using R (20 credits)
In this highly practical course unit, you will be introduced to the main building blocks of the R and RStudio software and develop skills in working with R and RStudio in an efficient manner. The unit will cover data management and how to prepare (and tidy) data prior to visualisation and analysis. You will use various R extensions (or ‘packages’) to facilitate different approaches to data exploration, visualisation, and investigation of relationships between variables. Incorporated practical examples will be based on real-world data from across the social sciences.
Knowledge and understanding
- State and define the basic concepts underpinning statistical programming
- Organise complex data management tasks in R and RStudio
- Outline the principles of the ‘grammar of graphics’
- Identify and explain the value and limitations of statistical programming
- Recognise the value of reproducible data practices
- Critically assess data workflows
Intellectual skills
- Devise advanced data management plans using statistical programming
- Produce well-reasoned arguments for the suitability of various management and visualisation approaches given different data types
- Evaluate ‘messy’ data and develop well-justified cleaning procedures
Practical skills
- Execute data recoding and reshaping tasks in R and RStudio to extract new insights
- Implement appropriate methods to quantify and assess relationships among variables in R and RStudio
- Create reproducible workflows with different types of data
Transferable skills and personal qualities
- Manage, recode, and reshape data in R and RStudio
- Summarise, visualise, and analyse data in R and RStudio
- Create ‘clean’, consistent, and well-organised R code
- Produce text output in various formats using RStudio
- Formulate, organise, express, and communicate data-driven opinions effectively
- Communicate statistical information using suitable and effective graphics
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2. Introduction to Statistical Modelling (20 credits)
This unit will introduce you to complex quantitative data analysis in the social sciences. It is designed to help you develop technical competence and robust foundations of the underlying principles of the statistical methods employed to interpret analysis output competently. You will use actual data from across the social sciences (e.g., politics, economics, psychology, sociology, criminology, etc.) to build your ability to conduct descriptive, exploratory, and inferential statistics.
Knowledge and understanding
- Describe the concept of statistical inference and the relationship between population, samples, and uncertainty
- Outline the assumptions of different statistical methods and recognise their practical value and limitations
- Discuss the challenges associated with causal inference in the social sciences
- Recognise the importance of conceptual and theory-driven research in the social sciences
- Formulate mathematical functions to represent statistical models
Intellectual skills
- Interpret and assess statistical output from a variety of methods
- Synthesize and critically evaluate academic literature
- Derive evidence-based arguments for/against competing hypotheses
- Develop a causal reasoning approach to hypothesis-driven research
Practical skills
- Describe, summarise, and visualise different data types using R and RStudio
- Utilise appropriate methods to quantify, model, and assess relationships between variables in R
- Interpret results of a variety of exploratory and inferential statistical methods
Transferable skills and personal qualities
- Critically assess quantitative research
- Utilise statistical programming software (R and RStudio) to interrogate data and draw valuable insights
- Formulate, organise, express, and communicate data-driven opinions effectively
- Communicate statistical information using suitable and effective graphics and statistical models
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3. Survey Methods and Online Research (20 credits)
In this unit, we'll introduce you to the principles of survey design for large and small-scale surveys and its contextualised application in academia, public and private sectors. It is intended to help you develop robust theoretical and practical foundations relating to the process of planning, designing, and conducting a survey, and the practical aspects of survey methodology, including ethical considerations. The course will likewise place emphasis on different sampling strategies, survey methodologies, the impact of challenging factors on survey data quality, as well as techniques to address these factors.
Knowledge and understanding
- Recognise the importance of conceptual and theory-driven survey designs
- Summarise ethical considerations associated with survey design and implementation
- Define key terminology utilised in survey design
- Outline factors which influence data quality
- Differentiate between sampling techniques and methods
- Discuss the importance of measurement error and estimation
- Describe the interconnected relationship amongst the different steps and components of survey research in the social sciences
Intellectual skills
- Assess suitability of and make evidence-based judgements about sampling techniques in the context of different research questions
- Evaluate survey questionnaires in the context of different survey designs
- Ability to critically assess the quality of surveys and survey data
- Specify well-reasoned arguments for selecting post-survey processing and estimation methods
Practical skills
- Design and plan a survey (online and in the field)
- Create questionnaires to collect suitable information for specific research questions, taking into considerations limiting factors for different types of designs
Transferable skills and personal qualities
- Prepare a survey proposal
- Formulate, organise, express, and communicate evidence-based opinions effectively
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4. Data Science Modelling (20 credits)
This unit aims to prepare you to handle high-dimensional and complex datasets in social sciences (e.g. criminology, politics, sociology, psychology etc.). It is designed to help you develop technical competence and robust foundations in and of the underlying principles of various supervised and unsupervised classification and forecasting methods to interpret analysis output competently. The unit will make use of real data from across the social sciences and will further develop practical skills in R and RStudio software. Ethical considerations will also be integrated throughout the course unit to further cement the integrity-based use of ‘big’ data.
Knowledge and understanding
- Summarise the necessary procedures for handling high dimensional and complex datasets
- Outline the principles underlying the methods and models addressing different classification and forecasting problems
- Discuss the ethical implications of machine learning and ‘big data’
Intellectual skills
- Ability to select the appropriate analytical tools given specific applications
- Interpret statistical output from a variety of methods and models
- Assess statistical techniques in the context of research question and data used
Practical skills
- Devise justified plans to handle large and complex datasets using R and RStudio
- Select and estimate supervised and unsupervised learning models in the context of empirical applications
Transferable skills and personal qualities
- Utilise statistical programming software (R and RStudio) to interrogate high-dimensional data and draw valuable insights using various machine learning techniques and models
- Formulate, organise, express, and communicate data-driven opinions effectively
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5. Multilevel and Longitudinal Analysis (20 credits)
This unit aims to extend your knowledge to complex survey designs and more intricate data structures in the social sciences. The unit will expand on the concepts, methods and models previously introduced and will further develop programming skills in R and RStudio. The unit will focus on models that can be used to analyse hierarchical data, such as cross-country data or longitudinal data. In this unit, you'll make use of real data originating from surveys of varying complexity to enable you to develop methodologically and statistically robust skills in tackling these complexities in practice.
Knowledge and understanding
- Recognise different types of nested data in the social sciences and how they impact modelling decisions
- Specify how longitudinal data can be used to answer key questions in the social sciences
- Distinguish between different methods for addressing nested and longitudinal data structures
Intellectual skills
- Interpret statistical output from a variety of advanced quantitative methods
- Appraise academic literature that uses nested and longitudinal data
- Select appropriate statistical methods to account for different nested and longitudinal data structures
- Defend methodological selection(s) using justified, evidence-based arguments
Practical skills
- Develop an appropriate design, plausible model, and appropriate method of analysis of nested and longitudinal data
- Utilise statistical computing software (R and RStudio) to implement various complex methods (e.g. multilevel model for change)
- Identify and implement suitable methods for interrogating complex data
- Interpret results of a variety of methods suitable for nested and longitudinal data
- Assess the quality, practical value, and limitations of estimated statistical models
Transferable skills and personal qualities
- Apply suitable statistical methods and models to gain valuable insights from complex data using R and RStudio
- Formulate, organise, express, and communicate data-driven opinions effectively
- Communicate statistical information using suitable and effective graphics
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6. Demographic Forecasting (20 credits)
Optional unit
This unit aims to provide you with the skills necessary to derive, interpret, and apply a range of demographic measures to past and present populations at various levels of geography. The unit will develop your ability to critically appraise accuracy and quality of various measures in the light of available data sources. The unit will make use of real data and focus on applying appropriate methods and critically interpreting outcomes such as those of the COVID-19 pandemic in the UK and other countries. Various measures of estimating and forecasting mortality will be emphasised as well other components of population change.
Knowledge and understanding
- Describe key concepts and theories related to population change and population components
- Discuss the measures used to analyse population change
- Recognise suitable methods and data commonly used to measure and forecast population change
Intellectual skills
- Select, summarise, and evaluate information from various sources as well as academic literature on demographic forecasting
- Critique methods used in measuring and forecasting population change
- Compose justified arguments for/against different statistical methodologies and take appropriate decisions regarding measuring population change in different contexts
Practical skills
- Produce a range of demographic measures using statistical techniques in R and RStudio software
- Assess quality of claims by media and statistical authorities about population change
Transferable skills and personal skills
- Utilise statistical computing software (R and RStudio) to interrogate data and draw valuable insights about population structure and population change
- Apply appropriate methods on real world data in various contexts
- Formulate, organise, express, and communicate data-driven opinions effectively
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7. Structural Equation Modelling (20 credits)
Optional unit
This unit aims to introduce you to the theoretical principles of structural equation and latent variable modelling and provide the required practical skills to run various types of models in R and RStudio. The course unit is designed to help you develop technical competence and robust foundations of the underlying principles of these methods to be able to competently interpret analyses output.
Knowledge and understanding
- Recognise the nature of structural equation modelling and its relationship to other statistical methods
- Distinguish between types of models used given different variables of interest
- Identify the contexts where different structural equation models are appropriate
Intellectual skills
- Evaluate latent variable and/or structural equation modelling published in scholarly journals
- Translate conceptual theories/hypotheses into appropriate latent variable and structural equation models
- Derive appropriate scientific inferences from the results of structural equation models
Practical skills
- Utilise R and RStudio to specify and fit a range of structural equation and latent variable models to social datasets
- Interpret the parameter estimates generated by different structural equation and latent variable models.
Transferable skills and personal qualities
- Synthesise evidence from relevant literature and individual analyses
- Formulate research questions and apply appropriate statistical models to address them
- Formulate, organise, express, and communicate data-driven opinions effectively
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8. Research Skills in Practice (20 credits)
Mandatory for MSc students
This course unit will provide you with the opportunity to strengthen your research skills in preparation for the 40-credit dissertation component of the MSc qualification. This course unit is comprised of two topics:
- Topic 1 will prepare you to develop theory-driven research hypotheses
- Topic 2 will comprise of approaches to producing an effective and impactful review of the literature in the social sciences.
The two topics will run in two blocks of 4 weeks and will be assessed independently.
Knowledge and understanding
- Demonstrate in-depth theoretical and practical knowledge and critical awareness of current topics of interest and debatein data analytics and social statistics
- Recognise the importance of a robust research framework in drawing valid, data-driven conclusions
Intellectual skills
- Identify and evaluate various statistical methods for addressing different research topics
- Apply critical appraisal skills to real world situationspertinent to the social sciences
Practial skills
- Find, evaluate, and synthesise information from scholarly journals and other sources of information
- Develop theory-driven research questions in the social science context
Transferable skills and personal qualities
- Develop a research proposal
- Conduct critique of academic literature and evaluate various information sources
- Formulate, organise, express, and communicate evidence-based opinions effectively
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Project (40 credits)
Mandatory for MSc students
To obtain a Master of Science (MSc), you will need to successfully complete the Research Skills in Practice (RSiP) unit worth 20 credits and deliver a 9,000-word dissertation worth 40 credits.
In your project, you will identify and investigate a research topic of interest relevant to professional practice in the social sciences. The dissertation should take the form of a quantitative research study that utilises secondary social data, preferably from a large-scale survey. Throughout the dissertation period, you will follow a recommended timeline and will receive support through frequent synchronous sessions with your assigned dissertation supervisor.
Knowledge and understanding
- Demonstrate broad and in-depth knowledge of current trends and debates in data analytics and social statistics
- Show critical engagement with research and scholarship
- Recommend improvements in social science professional practice and research agenda, including their own study
Intellectual skills
- Evaluate relevant bodies of literature in the social sciences
- Appraise different theoretical perspectives in the social sciences and make evidence-based assessments
- Identify an appropriate range of research methods for a chosen dataset
- Develop robust research questions or hypotheses
- Synthesise and evaluate findings
- Deliver clear evidence-based and data-driven conclusions and recommendations
- Exhibit critical reasoning through generating research findings that add to the existing body of knowledge in the social sciences
Practical skills
- Conduct literature reviews using a range of bibliographic techniques and sources
- Analyse, synthesise, and apply relevant concepts and methods to a chosen topic
- Design and conduct a research project using secondary data
- Write using appropriate format, structure, presentation, language, and tone suitable for academic audiences
Transferable skills and personal qualities
- Analyse and evaluate academic literature and other sources of information and evidence
- Apply problem-solving skills to identify and address research gaps using suitable methodological approaches
- Assess potential shortcomings in the research process and derive appropriate solutions to address these in a timely manner
- Illustrate high-quality writing using language appropriate to an academic audience;
- Recognise, evaluate, and draw appropriate conclusions given methodological limitations
- Utilise Information Technology effectively to search databases and other internet resources for literature and produce documents using word processing and presentation programs
Course structure
This flexible course is delivered 100% online to allow you to fit your study around your work and other commitments. It explores the fields of data collection, analysis and social statistics using real-world techniques and examples.
Throughout your study, there is ample opportunity for collaboration and networking with your course peers. You will enjoy a high level of support and expertise from your course academics. In this course, you will use the industry standard statistical software - R, allowing you to integrate your learning into your field of work.
This will also empower you to act as a data analysis expert within your workplace, sharing your knowledge to other colleagues for the benefit of the wider team and group projects.
Course learning aims
We've designed this course to create highly competent data analytics professionals who can confidently process data and identify trends across disciplines.
Through studying Data Analytics and Social Statistics, you will reach a high level of competence in data management using real data. You will understand the theoretical underpinnings of statistical methods and gain experience using microdata from different sources.
This course aims to equip you with the ability to critically appraise and carry out social data collection. You will develop a critical awareness of social science data and concepts and use your knowledge to develop original research using data analytics tools.
Through this course, you will be able to confidently present and write about data analytics, improving your skillset and allowing you to cross over to a new industry.
Teaching and learning
This is a flexible, online programme designed to fit around your existing commitments. There are 20 hours of study per week to take when it suits you. We have an extensive array of tools in our virtual learning environment (VLE) including videos, interactive workbooks, self-tests, online tutorials and online assessment.
You will also get to participate in events such as seminars with experts from leading organisations and engagement sessions with your course colleagues. In these sessions, you will have the chance to collaborate and build your network.
Our course academics are world-leading specialists in social science and research, with professional backgrounds analysing data across different disciplines.
Library services
As a student with The University of Manchester, you will be able to use our extensive library services. This will grant you access to books, e-books and journals about social statistics, quantitative data analysis and research, and data science, from introductory to advanced levels.
You will be assigned a dedicated Study Support Advisor who will be your first point of contact for study-related questions and help with the VLE.
Academic teaching start date for September 2024 entry is 2 September 2024.
The welcome event and induction take place one week before the academic teaching start date. Our admissions team will confirm your induction date closer to the time.
Please ensure that you complete your registration ahead of your chosen entry date to gain access to the online learning material and library services.
Coursework and assessment
All coursework and assessments are completed online involving different methods including individual and group reports, essays, project reports, presentations and quizzes. For assignments that require you to use statistical software, no special licenses are needed as we use R, which is available for free.
If you choose to study on the MSc level, you will also be assessed through the Research Skills in Practice (RSiP) unit and a 9,000-word dissertation. You will get the chance to explore a topic of your choice, contributing to social science and new, innovative interdisciplinary research.
Admissions information
From your initial expression of interest right through to graduation, you’ll receive all the support you need. We will guide you through the enrolment process and help with subject assistance, administrative logistics and fee options, online learning skills, workload management and special circumstances.
Entry requirements
Academic entry qualification overview
An Upper Second (2:1) class honours degree, or the overseas equivalent in a social science discipline.
We may also consider exceptional applicants with a Lower Second (2:2) class honours degree in a social science discipline (or the overseas equivalent), with either research experience or equivalent professional background.
If you chose to study on a PGDip level and would like to build your qualification to an MSc, you will have to obtain a 50% pass mark on taught units.
If you are an international student, and are looking for a general guide on entry and language requirements for your country, please visit our country-specific information pages .
English language
If you are not from, or did not graduate from a majority English speaking country , we will also require proof of your English language ability. If you have already taken an English language qualification, please include your certificate with your application.
- IELTS - overall score of 6.5 with no less than 6.5 in the writing component, or equivalent. Discover more about English language requirements .
English language test validity
Application and selection
How to apply
Advice to applicants
To speed up the application process, please submit the following documents with your online application form:
1. Copies of official degree certificates and transcripts of your previous study, showing the subjects taken and grades obtained. If these documents are in languages other than English, please provide official translations in addition to your official certificates and transcripts.
2. English language score report (if applicable) or alternative evidence to demonstrate your English language competency.
3. A copy of your CV detailing your full work experience.
4. Personal statement addressing the questions below (max 500 words)
- What attracts you to apply to this course?
- What do you hope to gain from this course and how will it help you achieve your aims?
5. As part of the application process, you will be asked to provide contact details for one referee, professional or academic. The University will contact your referee directly after you submit your application and direct them to complete our online reference form.
Scholarships and bursaries
If you're an English or EU student living in the UK, you may be eligible for a loan.
Manchester Master's Bursary (UK)
We're committed to helping students access further education.
Manchester Alumni Scholarship Schemes
If you completed your degree at Manchester, you could receive a discount.
If you're joining us from Uganda, Ethiopia, Rwanda or Tanzania, you can apply for this scholarship.
Funding for students with disabilities
If you have a disability, we can help you apply for relevant funding.
Fees and funding
Total course tuition fees for entry in September 2024 are:
- MSc - £16,000 (UK/EU/International)
- PGDip - £10,666 (UK/EU/International)
Please note the tuition fees are subject to an incremental rise in September.
Tuition fee discounts
- Early application discount (10%): Apply on or before 19 May 2024 to receive 10% reduction on your tuition fee. To be eligible, you will need to submit a complete application on or before 19 May 2024 and if offered a place, you will need to accept your offer within two weeks from the date of the offer.
- Alumni discount (15%): If you have successfully graduated from a credit-bearing qualification at The University of Manchester or UMIST, you can receive a 15% discount on the tuition fees that you are personally funding.
One-discount policy : Discounts and scholarships are not accumulative. If you qualify for more than one, you will be awarded the one that is the highest amount.
Employer funding
If you are looking to secure funding from your employer, we can help you build a business case or talk to your employer directly. Contact us on studyonline@manchester.ac.uk to arrange a consultation.
Payment by instalments
During registration you will have the opportunity to pay your fees in three equal instalments. Learn more .
Additional cost information
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).
Regulated by the Office for Students
The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website.
You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website.