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Data Science and Analytics MSc

Course Description

Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (eg SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

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 organization 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 core methods in data science application and research (eg 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 (eg NoSQL data stores) alongside cloud computing tools (eg 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 (eg clustering, regression, support vector machines, boosting, decision trees and neural networks).

Data Management and Business Intelligence

The aim of the module is to develop knowledge and skills to support the development of business intelligence solutions in modern organisational environments. Example topics of study include issues in data/information/knowledge management, approaches to information integration and business analytics. Practical aspects of the subject are examined in the context of the data warehousing environment, with a focus on emerging in-memory approaches.

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 (eg 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 (including Research Methods)

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.

Visit the Data Science and Analytics MSc page on the Brunel University London website for more details!

Entry Requirements

A UK first or second class Honours degree or equivalent internationally recognised and usually come from a scientific/engineering background and/or a numerate subject area.Applicants with other qualifications with industrial experience (that is relevant to the subject area) may be considered and will assessed on an individual basis and industrial certifications may be taken into account (such as those provided by organisations such as Microsoft and Sun for example). Such applicants may be required to attend an interview.Entry criteria are subject to change.

Course Fees

UK/EU students: £9,000 full-time; £4,500 part-time

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