Taught course

Data Science and Analytics

Institution
Brunel University London · Computer Science
Qualifications
MSc

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.

Months of entry

September

Course content

The aim of the programme is to develop a critical awareness of the state-of-the-art in data science and demonstrate the practical skills necessary to create value in its application to business, scientific and/or social domains.

Data is being collected at an unprecedented speed and scale. 'Big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply, however, and the shortage of skilled workers in the data analytics market is cited as a key barrier. This programme addresses that shortage, combining a strong academic programme with hands-on experience of leading commercial technology (and the chance to gain industry certification).

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.

Course Content

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).

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 (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

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.

Information for international students

English Language Requirements IELTS: 6.5 (min 6 in all areas) TOEFL Paper test: 580 (TWE 4.5) PBT TOEFL Internet test: 92 IBT (R20, L20, S20, W20) Pearson: 58 (51 in all subscores) BrunELT: 65% (min 60% in all areas)

Qualification and course duration

MSc

part time
24 months
full time
12/15 months

Course contact details

Name
Student Recruitment Coordinator
Email
cs-msc-courses@brunel.ac.uk
Phone
+44 (0)1895 265939