MSc Data Science
Year of entry: 2019
|Full-time||Part-time||Full-time distance learning||Part-time distance learning|
- Absorb and focus on data, and what data science can do.
- Develop your team-working skills, and actively study as part of a dynamic group.
- Be inspired by what the interdisciplinary course, drawing on five different disciplines, can provide.
On this day, you will find out more about the School of Social Sciences and our resources, and meet academic and admissions staff who will be able to answer any questions you have.
For more information, see open days and visits .
For entry in the academic year beginning September 2019, the tuition fees are as follows:
UK/EU students (per annum): £9,500
International students (per annum): £21,000
All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of courses lasting more than a year for UK/EU students (fees are typically fixed for international students, for the course duration at the year of entry). For general fees information please visit postgraduate fees .
Self-funded international applicants for this course will be required to pay a deposit of £1,000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.
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).
In addition, the Manchester Alumni Scholarship Scheme offers a £3,000 reduction in tuition fees to University of Manchester alumni who achieved a first-class Bachelors degree and are progressing to a postgraduate taught masters course.
For more information, see fees and funding or search the University's postgraduate funding database .
Courses in related subject areas
Use the links below to view lists of courses in related subject areas.
- Social Statistics
- Business and Management
- Computer Science
- Planning and Environmental Management
Academic entry qualification overview
- high 2:1 honours degree (or overseas equivalent)
Students whose first language or language of instruction is not English may be asked to provide evidence of fluency in English by achieving scores in English language tests, as follows:
- an overall score of 7 in IELTS, with a minimum of 6.5 in all components; or
- TOEFL (IBT) score of 100 with a minimum of 26 in all components.
- TOEFL code for Manchester is 0757.
Please note that CAS statements are issued only when all conditions of the offer have been satisfied, PDF copy of passport received and the offer accepted.
Applicants that do not meet these scores, may be eligible to do a pre-sessional English language course at Manchester (either 6 or 10 weeks), you will be required to successfully complete the course at the required level before you are permitted to register on your academic course.
English language test validity
Application and selection
How to apply
Advice to applicants
Applicants should, in their application, demonstrate aptitude, knowledge and/or interest in three areas:
- Data analytics and/or statistics
- Computational subjects
- Pathway specific requirements
These can be demonstrated by modules/courses taken at undergraduate level and high school level and/or professional experience.
Due to high demand for this course, we operate a staged admissions process with selection deadlines throughout the year, as follows:
- 7 January (decision by 15 February, accept offer by 15 March)
- 1 March (decision by 8 April, accept offer by 8 May)
- 1 May (decision by 1 June, accept offer by 1 July)
If we make you an offer, you will have approximately 4 weeks in which to accept (conditional and un-conditional offers). Any offers not accepted by the deadline will be withdrawn so that an offer can be made to another candidate.
All conditional offer holders will have until 1 August to satisfy the conditions of their offer.
Due to competition for places, we give preference to students with grades above our minimum entry requirements.
You need to ensure that you submit your supporting documents with your on-line application as it may delay us processing your application before the decision deadline.
Whilst we aim to give you a decision on your application by the decision date, in some instances due to the competition for places/volume of applications received, it may be necessary to roll your application forward to the next deadline date. If this is the case we will let you know after the deadline date.
Applications received after our final selection deadline will be considered at our discretion if places are still available.
We can accept your application before you complete your undergraduate studies; please submit your latest transcripts with your on-line application.
- all places are subject to availability and if you apply for one of the later dates, some courses may already be closed, we recommend that you apply early in the cycle to secure your place with us;
- meeting the minimum entry requirements does not guarantee an offer;
- if you are a current undergraduate student at the University of Manchester, you may be eligible to apply via the 'Fast-Track' scheme, email email@example.com for further information;
- international applicants who will require a visa to study in the UK can obtain up-to-date information on the latest student visa advice and guidelines;
- For a copy of the Postgraduate prospectus, email firstname.lastname@example.org .
How your application is considered
All applicants must submit the following:
- online application form;
- supporting statement;
- transcripts of degree; and
- Two references (please ask your referees to scan/email their references to email@example.com ).
Please note, applications will not be considered if one or more of the above documents are missing.
When assessing your academic record we take into account your grade average (both overall and for courses relating to the above requirements), position in class, references and the standing of the institution where you studied.
This unique and innovative Masters in Data Science offers you the opportunity to develop data science skills from a wide range of disciplines.
Our goal is to provide you with the skill base to become an agile scientist, who can work in a variety of settings and meet the challenges and reap the benefits of interdisciplinary team working. There is an acute shortage of skilled workers across the globe and this qualification will provide a strong boost to your employability profile.
The core modules are:
- Machine learning and statistics (parts 1 and 2)
- Understanding databases
- Understanding data
- Applying data science
Pathways are defined through three pathway-specific modules, plus a dissertation. The five current pathways are:
- Applied urban analytics
- Computer science data informatics
- Management and business
- Social analytics
Our aim, is to provide you with training in core data science skills in a disciplinary context. You will finish the programme with competencies in the following areas:
- Computational skills
- Data analytical skills
- Data stewardship skills and knowledge
- Project design skills
Teaching and learning
Teaching and learning will be in a mixture of formats, including:
- Lab classes
- Student-lead presentations
- Group work
- Case studies
Coursework and assessment
A mixture of assessment methods are used, including:
- Practical assignments
- Individual and group reports
- Consultancy simulation
Course unit details
Understanding Data and their Environment
This module is a combination of technical and non-technical topics related to critical externalities to the data analytics process. The primary aim of the module, is to demonstrate that data science cannot be carried out in a vacuum, due to a range of considerations affecting our ability to carry out research. However, appropriate management of these, can lead to higher quality and more responsible research.
The course has four components:
- Ethics and the law: data protection, anonymisation, statistical disclosure, understanding consent.
- Information about data: metadata and paradata. Provenance and data generating processes; Issues about data quality and the impact on inference; accessing and finding data.
- Pre-Processing: understanding data quality and divergence and the impact on inference; Cleaning data; Editing and imputation models.
- Combining and enhancing data: basics of data linkage/integration.
Statistical foundations includes a mixture of lectures designed to communicate key ideas in statistics and machine learning with practical sessions. You can - in simple cases - develop tools in Python and, where appropriate, other industry standard languages.
There are five main sections:
- Thinking probabilistically
- Exploratory data analysis
- Statistical estimation
- Comparison and selection of models
- Special Topic: one special topic will be chosen to demonstrate the general concepts in more depth. An example is: time series with applications in Biology and Finance: stationarity, trends, filtering and seasonal adjustment, autocorrelation, linear predictors and autoregressive moving average (ARMA) models.
This module is delivered as a mixture of lectures and practical sessions, and has five main sections:
- Dimension reduction and feature extraction
- Classifiers and clustering
- Markov-chain Monte Carlo (MCMC) methods
- Special Topic: one special topic will be chosen to go into near-research depth, e.g. Random Forests; Social Networks; Advanced Monte Carlo methods.
The course unit will introduce you to data management principles and how data management can be used to address business problems.
You will learn the theory of data modelling as the underlying theory to propose data management solutions to practical problems. In terms of databases, both relational and non-relational databases will be taught and will be used to demonstrate how they can be deployed within a business context. Industry standard database software will be used and you will develop practical skills on how they can be designed, implemented and deployed.
Applying Data Science
This course unit is delivered with a practical focus, and will centre on delivering a project with the industry partner.
You will have a series of surgery sessions to receive support from lecturers on your work. Lectures and workshops will be delivered by academics and professional experts, invited to lecture on specific topics. Course work will consist of a real world practical project with a brief designed by lecturers and industry partners. Industry partners will be existing or new partners to the schools delivering the programme. You will apply the concepts and methods using the project as the main example.
Applied Urban Analytics
We would expect evidence of interest and/or experience in topics related to urban analysis. Examples are:
- Work experience in urban-themed topics (e.g. data analyses for spatial phenomena, public policies or real estate market analysis)
- Experience in working in spatial or GIS-based data analysis
- Evidence of training in urban-themed methods or topics (e.g. GIS).
Business and Management
We normally expect you to hold a degree in a quantitative or computational subject such as mathematics, statistics, management science or economics, physics, engineering or computer science. Applicants with extensive business and management industrial experience combined with an honours degree in a quantitative subject may also be considered for admission.
Computer Science Data Informatics
You should have a strong background in computer science reflected, for example, in solid programming and software development skills. We typically expect a First or strong Upper Second class honours degree, or the overseas equivalent, in computer science, or in a joint degree with at least 50% computer science content. We may consider a lower proportion where a student has performed consistently strongly in their computer science modules. Applicants with extensive computer science industrial experience and an honours degree in computer science, or its overseas equivalent, may also be considered for admission.
You are expected to have an undergraduate degree with a substantial amount of mathematics including probability and statistics. As a minimum, you should have done calculus or mathematical analysis, linear algebra, two courses in probability and two courses in statistics. A Mathematical Statistics course may count as one probability and one statistics course depending on the syllabus. If your course is called advanced mathematics or similar, then we need to know how much calculus/linear algebra it contains.
You can have a look at what Manchester students do in the first two years, or refer to the following list for a little more detail:
- Calculus or Mathematical Analysis (functions of a single and several variables, continuity, derivatives, integrals, Mean Value Theorem, Taylor series expansion, minimisation and maximisation, Lagrange multipliers)
- Linear Algebra (linear independence, determinant, inverse, eigenvalues and eigenvectors)
- Probability I (probabilities and conditional probabilities, Bayes Theorem, moments)
- Probability II (multivariate and conditional distributions, generating functions, Law of Large Numbers and Central Limit Theorem)
- Statistics I (descriptive statistics, normal, t, chi¿square and F distributions, significance tests)
- Statistics II (Maximum likelihood estimation, Likelihood ratio tests, simple regression and analysis of variance).
You should have a degree with a substantial proportion of social science content. As a minimum, you should have completed two degree level courses on topics from any of the following: sociology, psychology, anthropology, economics, history, human geography, political science, public health.
The following includes other skills/experience that would increase the chances of being selected:
- Evidence of applying statistical modelling to social sciences
- Non-academic experience with the application of statistical models to social issues
- Experience working on public policy issues or similar
Course unit list
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
|Applied Spatial Analysis for Planning||PLAN60761||15||Optional|
|Survey Research Methods||SOST60421||15||Optional|
|Longitudinal Data Analysis||SOST70022||15||Optional|
|Complex Survey Designs and Analysis||SOST70032||15||Optional|
|Structural Equation and Latent Variable Modelling||SOST70042||15||Optional|
|Social Network Analysis||SOST71032||15||Optional|
Our goal is to provide the skill base for a new type of data scientist. One who is adept at working in variety of settings and can meet the challenges of interdisciplinary working. There is an acute shortage of skilled workers across the globe, so, this qualification will boost your employability profile.
As a School of Social Sciences postgraduate at Manchester, you will have access to a wide range of careers support tailored to your career or further study.
For more information, see Careers and Employability .