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Data Science and Analytics

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MASTERS

Data Science and Analytics

Brunel University London

London, Great Britain

Full-time

€12,929.44/year

Duration

1 Year

Language

English

Program Description

From social networks, ecommerce and government through to sensors, smart meters and mobile networks, 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 to unlocking everything that data has to offer.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic degree course with hands-on experience of leading commercial technology, and the chance to gain industry certification. You will develop both your critical awareness of the very latest developments in data science and the practical skills that help you apply data science more effectively in a wide variety of sectors including finance, retail and government.

You’ll gain knowledge of key concepts and the nuances of effective data analysis. You’ll gain confidence in your own critical understanding of the challenges and issues arising from taking heterogeneous data at volume and scale, understanding what it represents, and turning that understanding into insight for business, scientific or social innovation. You’ll develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.

The course is designed to offer you the opportunity to gain hands-on experience in several data analytics tools (e.g. Hadoop, Spark, Tableau), programming languages (R, Python) and machine learning libraries.

A series of sessions in Python is offered to support students who are less familiar with programming.

You will also have the opportunity to obtain an SAS certificate such as SAS Base Programming, which is a recognised industry qualification, following a two-week SAS certification ‘boot camp’.

Entry Requirements

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Education

Bachelor's Degree

Degree from State Technical Institute

6.5

English Level

Michigan Proficiency (ECPE)

Cambridge Proficiency (CPE)

Cambridge Advanced (CAE)

International Baccalaureate

5.0

TOEFL (internet based)

IELTS

6.5

PTE

Pearson Test of Academic Engl.

TOEFL (paper based)

570

Required Documents

Certified Copy of the Degree Certificate/Diploma

Certified Copy of the English Certification

ID Certified Copy

Passport Certified Copy

Personal Statement - Motivation Letter

Portfolio Relevant to the Programme

Detailed Transcript

Student Visa

Notes:

Information about requirements:

A 2:2 or above UK Honours degree or equivalent internationally recognised qualification from a scientific, engineering, or a numerate subject.

Other accepted qualifications:

We welcome applicants with other qualifications and industrial experience (relevant to the subject area), who will be assessed on an individual basis. Such applicants may be required to attend an interview and/or aptitude tests.

Other accepted English qualifications:

BrunELT (Brunel English Language Test): 65% (min 60% in all areas)
Trinity ISE
LanguageCert
City & Guilds ESOL, IESOL adn SESOL (no expiry)
European Baccalaureate Year 7 (Except English Language 4)
Duolingo

Course Content

This incredibly relevant and current course will equip you with all the skills you need to venture out into the world of analytics and big data.

Teaching and Learning

Our Data Science and Analytics master's programme aims to equip you with the qualities and transferable skills necessary for employment. The course is developed with industry in mind and has one or more industrial advisers who are involved in course development and delivery.

Modules are typically taught via lectures and seminars with some lab work. Where appropriate other teaching methods will also be incorporated. All learning is supported by the market leader in Virtual Learning Environments (VLE), the Blackboard Learn system.

Quantitative Data Analysis

The aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core statistical methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

Modern Data

The aim of this module is to provide an introduction to data management and exploration. An overview of current industry standard processes to modern data analysis will be presented, and you will learn to design and plan a predictive analytics project. Basic concepts of data management and retrieval will be discussed. Well established strategies and approaches to data understanding, data preparation and cleaning will be presented.

Data Visualisation

The aim of this module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets to, for example, decision makers. Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design. The role of interactivity within the visualisation process will be explored and an emphasis placed on visual storytelling and narrative development.

Research Project Management

This module aims to develop and deploy the skills necessary to design a scholarly piece of research work to address an identified problem area within the chosen field of study.

Ethics and Governance of Digital Systems

This module aims to develop a critical understanding of topics related to the handling and governance of digital information in contemporary systems contexts. Such topics will include the way that networked and intelligent systems are designed and used; the motivations for their adoption; the substantive issues arising; and approaches to their regulation and governance. Examples from the public and private sectors will be used to illustrate these developments.

Machine Learning

The aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from heterogeneous data sets using statistical learning. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the processing of heterogeneous data. Content will cover machine learning techniques, such as principal component analysis, cluster analysis, decision trees and random forest, support vector machines, as well as approaches to performance evaluation.

Digital Innovation and Strategy

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).

High Performance Computational Infrastructures

This module aims to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. You will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content covers highly scalable cloud computing tools, for example Hadoop, and in-memory approaches, such as Spark.

Dissertation

The dissertation aims to develop and demonstrate advanced knowledge and skills in an agreed topic area related to the studied master's programme. As preparation for the dissertation, you will be given grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.