MSc Data Science / Course details
Year of entry: 2019
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|