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.
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, and, in particular, further PhD study at Middlesex.
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.
This course 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.
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.
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.
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.
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.
You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.
You will gain knowledge and understanding through a combination of traditional lecture delivery, small group discussions, small group and individual exercises, lab sessions and the individual project. Throughout your studies, you are encouraged to undertake independent study both to supplement and consolidate what is being learned, and to broaden your individual knowledge and understanding of the subject. Critical evaluation and selection of techniques and solutions will engage you in relating theory to practice.
Your technical skills will be assessed throughout the year in a series of formative and summative coursework. Every week, you will be given lab tasks designed to match the content covered in the lecture. These tasks are expected to be completed during the lab and you will receive timely feedback assessment.
Summative coursework is planned for every two months, after the completion of each major module component. The type of the work will depend on the module finished so it could range from development work after a technical component or a research/report after a non-technical component (such as design and evaluation). These works require considerably more effort than the formative coursework and can give you a clear indication of your performance on each major module component.
Upon completing the course, you will be well placed to step into a career as a data scientist in a wide range of industries and companies.
You could also consider continuing your studies to PhD level.
Dr Windridge heads the University's Data Science activities. His research interests centre on the related fields of machine-learning, cognitive systems and computer vision. He also has a former research interest in Astrophysics. He has played a leading role on a number of large-scale machine-learning projects in academic and industrial research and has won a number of interdisciplinary data science research grants areas such as psychological modelling and proteomics. He has authored more than 100 peer-reviewed publications (including best paper awards), with over 1000 citations collectively.