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Masters
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Computer Science & IT
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Master in Data Science and Analytics (MSc) - Brunel
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MASTERS

Master in Data Science and Analytics (MSc) - Brunel

Brunel University London
LondonUK
On campus
Full-time
€29,206.87/year
 4408 Points
Duration
1 Year
Language
English
Apply date
Jul 2024
Start date
Sep 2024

International Scholarships for September 2024 (Brunel)

Brunel University London offers scholarships for this postgraduate program. Apply through Brive until 31st July.

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Program Description

The master in Data Science and Analytics (MSc) provides skills, combining a strong academic degree course with hands-on experience in leading commercial technology, and the chance to gain industry certification. In the master in Data Science and Analytics (MSc), 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.

Entry Requirements

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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.

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Teaching and Learning

The master in Data Science and Analytics (MSc) 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.

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Curriculum

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.

Jobs - Careers

Companies seeking to employ the data science graduates include:

  • Accenture
  • AstraZeneca
  • AXA Insurance
  • British Airways
  • Capgemini
  • Experian
  • FICO
  • GE Healthcare
  • HSBC
  • Orange Pay Pal
  • Sopra and Waitrose
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