Course description

The range of pathways reflects the interdisciplinary nature of the programme and we welcome applications from students with backgrounds in a range of disciplines including:

business and management;

health science;

social sciences;

geography;

planning;

computer science; and

Mathematics.

We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, you will develop:

computational skills;

data analytical skills;

data stewardship skills and knowledge; and

Project design skills.

Aims

This innovative MSc in Data Science course is an opportunity for graduates from a broad range of disciplines to develop data science skills. Our goal is to help you develop into an agile, skilled data scientist, adept at working in variety of settings and able to meet the challenges and reap the rewards of interdisciplinary team work.

The range of pathways reflects the interdisciplinary nature of the course and we welcome applications from students with backgrounds in a range of disciplines including:

business and management;

health science;

social sciences;

geography;

planning;

computer science; and

Mathematics.

We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, you will develop:

computational skills;

data analytical skills;

data stewardship skills and knowledge; and

Project design skills.

Course unit details

Through a set of core units you will develop a set of key data science skills. The core units are:

Machine Learning and Statistics (both semesters)

Understanding Databases

Data Husbandry

Professional Skills and Practice

Applications in Data Science

In semester 2 you will be able to specialise, choosing one of our five pathways and then writing a final dissertation. The pathways are:

Applied Urban Analytics

Computer Science Data Informatics

Management and Business

Mathematics

Social Analytics

Applied Urban Analytics

We would expect applicants to evidence an interest and/or experience in topics related to urban analysis. Examples are:

Working 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 applicants 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

We require that all applicants 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.

Mathematics

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).

Social Analytics

You are expected to 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