Please note Middlesex University will be closing its Malta campus in September 2022. The programme you are interested in is open for 2019/20 entry because it can be completed during that period. We encourage you to discuss your application with our Admissions team who will be happy to answer any enquiries.
All industries now utilise data and Data-Science and Data-Analytics are increasingly identified as key industrial activities. The position of Data Scientist is rapidly becoming a required post for any company that wishes to take full advantage of the data that they collect. This course is designed to give you the skills to step into a career as a Data Scientist in a wide range of industries and companies.
Why study MSc Data Science* at Middlesex University?
This masters has been designed to offer those with a familiarity in maths, science or computing an opportunity to develop a key set of skills for future employment in a way that builds on your existing knowledge and skill base. Upon completing the course, you will be ready to fulfil the requirements of a Data Scientist.
You will focus on the intertwining areas of machine learning, visual analytics and data governance, and be able to strike a balance between theoretical underpinnings, practical hands-on experience, and acquisition of industrially-relevant languages and packages. You will also be exposed to cutting-edge contemporary research activity within data science that will equip you with the potential to pursue a research-based career.
Your studies will focus on the intertwining areas of machine learning, visual analytics and data governance. You will investigate theoretical underpinnings while gaining practical hands-on experience. You will build on your existing knowledge and skill base to gain key understanding that will be readily applicable for a career in data science.
Modelling, Regression and Machine Learning (30 credits) - Compulsory
This module will equip you with the theoretical and algorithmic basis for understanding learning systems and the associated issues with very large datasets/data dimensionalities. You will be introduced to algorithmic approaches to learning from exemplar data and will learn the process of representing training data within appropriate feature spaces for the purposes of classification. You will also focus on basic data structures and algorithms for efficient data storage and manipulation. The major classifier types are taught before introducing the specific instances of classifiers along with appropriate training protocols. You will explore where classifiers have a relationship to statistical theory as well as notions of structural risk with respect to model fitting. You will be equipped with techniques for managing this in practical contexts.
Visual Data Analysis (30 credits) - Compulsory
This module provides an understanding of the methods, theories and techniques relevant to interactive visual data analysis. You will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. You will gain experience in researching, designing, implementing, and evaluating your own visual analysis solutions, using both off-the-shelf tool-kits and data visualisation programming libraries. You will gain the knowledge to support your future employment or research in the fast-developing areas of data science, particularly visual analytics.
Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution (30 credits) - Compulsory
This module will give you an in-depth understanding of the tools and systems used for mining massive data-sets. It also serves as an introduction to the fascinating and emerging field of Data Science. You will focus on the language R, a statistical learning language used to learn from data, which will provide an overview of the most common data mining and machine learning algorithms. Each concept discussed is also accompanied by illustrative examples written in R language. You will be introduced to MapReduce, a programming model used to process big data sets and you will learn how to design good MapReduce algorithms to process massive datasets. You will also explore cloud computing systems and learn to use them effectively.
Legal, Ethical and Security Aspects of Data Management (30 credits) - Compulsory
Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services, amongst others. The volume of data collected, stored and processed brings many concerns especially related to privacy, data protection, liability, ownership and licensing of intellectual property rights and information security. As such, this module will focus on legal, ethical and security requirements that underpin the technical processes and practice of data science including the collection, preparation, management, analysis and interpreting of large amounts of data. You will explore how data can be fairly and lawfully processed and protected by legal and technical means. You will gain a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and security management policies that impact on the practice of data science. You will also be equipped with the necessary foundations to develop high professional standards when working as data scientists.
Individual Data Science Project (60 credits) - Compulsory
This module aims to develop your knowledge and skills required for planning and executing data science research projects, which can include proof of concept projects or empirical studies related to data obtained from industrial or academic sources. You will plan and carry out your project by applying theories, methods and techniques previously learned and critically analyse and evaluate your research results. You will develop your communication skills to competently communicate your findings in written and oral form
A 2:2 honours degree in a related subject, such as those providing a significant exposure to information technology
Applicants with degrees in other fields who can demonstrate relevant industrial experience may also be considered
Academic credit for previous study or experience
If you have relevant qualifications or work experience, academic credit may be awarded towards your Middlesex University programme of study. For more information about this, please get in touch with our admissions team on ; email@example.com
English language requirements for international students
You must have competence in English language to study with us. The most commonly accepted evidence of English language ability is IELTS 6.5 (with minimum 6.0 in all components)