BRUNEL UNIVERSITY LONDON

Data Science and Analytics

MSc  |  Placement Year:   No

Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.

You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.

The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

Programme description

Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.

You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.

The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

Key Modules

Our Master's programmes aim to equip you with the qualities and transferable skills necessary for employment. Each course is developed with industry in mind and has one or more industrial advisers who are involved in course development and delivery.

The ability to generate effective insight and value from data is increasingly important across all industrial sectors. Data science is thus becoming a feature in a very wide range of industries, including automotive, banking and financial services, energy (e.g. oil and gas), health, management consulting, media and new media, retail and transport.

Given the range of vertical sectors that data science is important to, there are a vast number of companies seeking to employ graduates in this area. These include such organisations as Accenture, AstraZeneca, AXA Insurance, British Airways, Capgemini, Experian, FICO, GE Healthcare, HSBC, nPower, Orange, PayPal, Sopra and Waitrose.

The roles that our graduates are typically recruited to within these organisations include analytics consultant, big data engineer/scientist, business analyst, clinical data scientist, data design specialist, data scientists, developer/development engineer, enterprise/technical architect, forecast analyst, marketing/customer and/or insight analyst, quantitative analyst and web analyst.

Module & Subject

Digital Innovation

The aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organisation with the changing demands of its business environment (or to capitalise on business opportunities). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.

Quantitative Data Analysis

The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of compulsory methods in data science application and research (e.g. bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

High Performance Computational Infrastructures

The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (e.g. NoSQL data stores) alongside cloud computing tools (e.g. Amazon EC2) and in-memory approaches.

Systems Project Management

This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.

Big Data Analytics

The aim of the module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques (e.g. clustering, regression, support vector machines, boosting, decision trees and neural networks).

Research Methods

This module will introduce methods of data collection and analysis when conducting empirical research. This research can take place in an organisational setting. Both in the private or the public sector. This module is essential preparation for the dissertation.

Data Visualisation

The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (e.g. to decision-makers). Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design and augmented reality. An emphasis is also placed on visual storytelling and narrative development.

Learning Development Project

The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).

Dissertation

Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.

As preparation for the dissertation, you will be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research

Entry Requirements
  • A minimum score of 55% - 65% or 2.75/4 - 3.25/4. Offers within the grade range are determined by the higher education institution attended.
  • IELTS: 6.5 (min 6 in all areas)
  • Pearson: 58 (51 in all subscores)
  • BrunELT: 65% (min 60% in all areas)
Foundation Campus
No Foundation
Course Option
Course Duration: 1 years
Course Fee:  18000.00
Course Level:  POSTGRADUATE
Application Deadline 
International Student:   (15,July)
Location
Country:  UNITED KINGDOM
Campus Location:  Brunel University London, Kingston Lane Uxbridge Middlesex UB8 3PH
Intake Deadline
SEPTEMBER