You are viewing this course for 2021-22 entry. 2022-23 entry is also available.

MSc
2021 start September 

Data Science

Information School, Faculty of Social Sciences

All organisations face the challenge of how to analyse and use data. On this course, you'll learn data science concepts and how to support data-driven approaches to problem-solving within complex organisational and societal contexts.
Students and lecturer

Course description

This course is no longer accepting applications for 2021 entry.

Gain an in-depth understanding of the theory and practice of Data Science and its application in different organisational contexts. You'll be provided with a set of fundamental principles that support ethical extraction of information and knowledge from data. Case studies will help to show the practical application of these principles to real life problems.

The programme is centred on three key aspects of data science: fundamental data-related principles, supporting infrastructures and organisational context. You'll gain practical skills in handling structured and unstructured data, analysing and visualising data, data mining, as well as gaining hands-on experience of software tools used and their use in real-world settings. You'll gain the skills of a data manager who understands what the algorithms (e.g., for data mining or handling ‘Big Data’) can do and when to use them for the benefit of the organisation.

Throughout the programme, there will be opportunities to gain hands-on experience using a variety of tools, such as R, Python and SPSS, Weka or Tableau/Spotfire and, although you're not required to have any knowledge of these tools before starting the course, it may be advantageous to read up on them in advance. You'll conduct in-depth research into your particular areas of interest for your dissertation.

Accreditation

CILIP accredited

Modules

You’ll need 180 credits to get a masters degree, with 45 credits from core modules, 75 credits from optional modules and a dissertation (including dissertation preparation) worth 60 credits.

Core modules:

Data Visualization

Visualization is a crucial technique to summarise data in an intuitive fashion. It can provide insights that are difficult to extract from the raw data. Because of this, visualization is often used to enhance the delivery of information in the media and in reports. The module will focus on the theoretical frameworks to design visual elements that are able to provide information about a data set. It will cover how to create and critique different visualizations to display data, as well as design considerations and good practices in data visualization.

15 credits
Introduction to Data Science

Data science is an emerging field that seeks to discover and explore new ways of exploiting data to support decision-making for a range of domains and problems. With individuals and organisations producing vast amounts of real-time heterogeneous data (i.e. Big Data), there is greater demand than ever to manage and analyse data effectively. This module aims to introduce students to the concepts and theories that underpin data science, provide an understanding of how they are used and impact on organisations, and gain hands-on experience with analysing and presenting data effectively using R and R Studio.

15 credits
Data Analysis

This module provides an introduction to analysing data using statistical methods, e.g., descriptive, bivariate and multivariate analyses. It will demonstrate different ways of analysing data, presenting the results of analyses (for example, graphically and using tables and text) and interpreting their meaning. Students undertaking the module will gain practical experience in using SPSS. By the end of the module students will be able to demonstrate an understanding of the concepts and theories of statistical data analysis, describe and use a variety of statistical methods, present the outputs of data analysis in an appropriate way and be able to use statistical software. The module will involve lectures, practical classes and student-led seminars which, together with self-directed learning, will cover the conceptual, theoretical and practical aspects of data analysis.

15 credits
Data and Society

The module draws upon key concepts and emerging debates from across the social sciences to address how social and political factors interact with (big) data and evolving data science techniques such as data mining, visualisation and analytics. Key issues and debates will be examined in relation to developments in fields such as marketing, political campaigning, and state security. The module complements more technical and management orientated modules, and aims to aid students in becoming more critical and reflective data scientists, decision makers and/or citizens able to successfully navigate the challenging social, political, legal and ethical issues related to data processing and use, and to reflect critically on the ways in which emerging data practices are shaped by and contribute to the development of complex social worlds.

15 credits
Data Mining and Visualisation

As the volume of and types of information collected and stored in databases grows, there is a growing need to gain new insights into the data by identifying important patterns and trends, and summarising the findings to inform decision making. Such insights can provide huge economic value and competitive advantages. Data Mining is the process by which this is done. This module will examine fundamental algorithms for clustering and classifying data as well as data visualisation strategies and data mining applications. Students will be introduced to key themes in data mining, including types of data mining problem (e.g. classification, clustering, rule mining), common algorithms used in machine learning (e.g. SVM, decision trees, k-means), feature selection and evaluation issues (e.g. measures and standardised benchmarks). It will also explore Data Visualisation (and visual analytics), the graphical representation of information that provides a qualitative understanding of the information on which decisions can be based. The last part of the module will present different applications of data mining to improve businesses (e.g., opinion mining) and user experience (e.g., recommender systems). Case studies will be used throughout the module to highlight the use of data mining methods and visualisation for tackling real-world problems.

15 credits
Database Design

Effective data management is key to any organisation, particularly with the increasing availability of large and heterogeneous datasets (e.g. transactional, multimedia and geo-spatial data). A database is an organised collection of data, typically describing the activities of one or more organisations and a core component of modern information systems. A Database Management System (DBMS) is software designed to assist in maintaining and utilising large collections of data and becoming a necessity for all organisations. This module provides an introduction to the area of databases and database management, relational database design and a flavour of some advanced topics in current database research that deal with different kinds of data often found within an organisational context. Lectures are structured into three main areas:¿An introduction to databases¿The process of designing relational databases¿Advanced topics (e.g. data warehouses and non-relational databases)The course includes a series of online tasks with supporting `drop in¿ laboratories aimed at providing you with the skills required to implement a database in Oracle and extract information using the Structured Query Language (SQL).

15 credits
Dissertation

This module enables students to carry out an extended piece of work on an approved information management topic, so that they can explore an area of specialist interest to them in greater depth. Students will be supported through tutorials with a project supervisor, will apply research methods appropriate to their topic, and implement their work-plan to produce an individual project report. Students will already have identified a suitable topic and designed a project plan in the pre-requisite unit Research Methods and Dissertation Preparation.

45 credits
Research Methods and Dissertation Preparation

This module assists students in the identification of, and preparation of a dissertation proposal. Students will: learn about: on-going research in the School; identify and prepare a dissertation proposal; carry out a preliminary literature search in the area of the dissertation research topic; and be introduced to the use of social research methods and statistics for information management.

15 credits

Optional modules - two from:

Business Intelligence

The module aims to provide students with an understanding of the way in which business people use information and why they use information. Students will study the key channels and sources that may be used, and key issues concerning the value of information and library services within business. The module will concentrate primarily on external information resources. Students will learn through a combination of lectures and practical exercises, with opportunities to use business-focused electronic information services.

15 credits
Information Governance and Ethics

The purpose of this module is to investigate topics related to the handling and governance of digital information and data in organizational and networked contexts. This will include an exploration of a) substantive issues and concerns e.g. accountability, decision-making, freedom, identity, intellectual property, openness, privacy, risk, security, and surveillance b) the design and use of relevant technologies e.g. Internet, DPI, digital rights, open source, P2P, social media c) systematic approaches and frameworks used in the regulation, governance and use of information in organizational and networked contexts e.g. copyright/left, data protection, freedom of information etc. Examples from business, government, health, law, and technology illustrate the topics investigated

15 credits
Researching Social Media

The module will examine the key theoretical frameworks and methods used in social media studies. Students will explore the following questions: 1) What can be learnt about society by studying social media? 2) How should researchers construct ethical stances for researching sites such as Facebook and Twitter? 3) What are the traditional and digital research methods and tools that can be applied to conduct research on social media? 4) What are the strengths and weaknesses of these methods?

15 credits
Big Data Analytics

Data Science techniques often need to be applied to large amounts of data to generate insights. To deal with volume, velocity, and variety of data we need to rely on novel computational architectures that focus on scaling-out data processing as compared to the classic scale-up approach. Such systems allow to add computational resources to a distributed system depending on requirements and load which changes over time. In this module we will give students knowledge about modern scale-out system architectures to perform data analytics queries over very large structured/unstructured datasets as well as to run data mining algorithms at scale.

15 credits
User-Centred Design and Human-Computer Interaction

Interface design and usability are central to the experience of interacting with computers. The module introduces usability principles and the design process for interactive systems exploring four major themes. Firstly, user psychology and cognitive principles underlying interface design. Secondly, user interface architectures, modes of interaction, metaphors, navigational structures. Thirdly, the user interface design process including task analysis, modelling constructs and prototyping techniques. Fourthly, the evaluation of user interfaces covering concepts of usability, goals and types of evaluation. The module focus is on the underlying principles of HCI and user-centred design approach with practical sessions to demonstrate these principles.

15 credits

The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.

Teaching

A variety of teaching methods are used combining lectures from academic staff and professional practitioners with seminars, tutorials, small-group work, and computer laboratory sessions. There is a strong emphasis on new ways of exploiting data to support decision-making for a range of domains and problems in an organisational context. In addition to the taught components, you will be expected to engage in independent study, reading and research in support of your coursework.

Teaching consists of two 15-week semesters, after which you'll write your dissertation.

Assessment

Assessments are designed to test your grasp of the theoretical principles, technologies and frameworks used to collect, store, analyse and exploit data; they may include essays, report writing, oral presentations, in-class tests and group projects.

There is a dissertation of 10-15,000 words, which provides you with the opportunity to focus in depth on a topic of your choice with one-to-one supervision. Opportunities exist for both project and dissertation studies to be carried out with our collaborating organisations, which can provide the opportunity to tackle real-life problems.

Duration

  • 1 year full-time
  • 2 years part-time
  • 3 years part-time

Entry requirements

You’ll need at least a 2:1 in any subject.

Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.

We also accept a range of other UK qualifications and other EU/international qualifications.

If you have any questions about entry requirements, please contact the department.

Apply

This course is no longer accepting applications for 2021 entry.

Any supervisors and research areas listed are indicative and may change before the start of the course.

Our student protection plan

Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.

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