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.
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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.
We’ve made sure that the skills and knowledge that you’ll gain on your course will not change during the coronavirus outbreak. If you’re applying to start this course or progressing into year one, two or three this autumn, your module information is below.
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 course will provide an in-depth of the tools and systems used for mining massive dataset and, more in general, an introduction to the fascinating emerging field of Data Science. The module is divided in two parts: The first part focuses on the language R, a statistical learning language used to learn from data. This part provides an overview of the most common data mining and machine learning algorithms and every discussed concept is accompanied by illustrative examples written in R language. The second part of the module takes a tour through cloud computing and big data systems and teaches the participant how to effectively use them. Specifically, platforms and systems like OpenStack, Hadoop, MapReduce, MongoDB, Spark and NoSQL databases are introduced and every concept is accompanied by a number of illustrative examples.
This module focuses on legal, ethical and security requirements that underpin the technical processes and practice of data science (the collection, preparation, management, analysis and interpreting of large amounts of data called big data). Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services among 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. This module will explore how data can be fairly and lawfully processed and protected by legal and technical means. It will give students a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and important information security management policies that impact on the practice of data science. Further it will equip student with the necessary foundations to develop high professional standards when working as data scientists.
The project module aims to develop your knowledge and skills required for planning and executing research projects such as proof of concept projects or empirical studies related to data science. To plan and carry out your projects you will have to:
See the course specification for more information:
Optional modules are usually available at levels 5 and 6, although optional modules are not offered on every course. Where optional modules are available, you will be asked to make your choice during the previous academic year. If we have insufficient numbers of students interested in an optional module, or there are staffing changes which affect the teaching, it may not be offered. If an optional module will not run, we will advise you after the module selection period when numbers are confirmed, or at the earliest time that the programme team make the decision not to run the module, and help you choose an alternative module.
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 (all taking place online while COVID-19 arrangements apply).
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.
We’re planning to deliver our assessment in a similar way to previous years. We will review this regularly, and let you know in advance of your assessment if we need to make any changes.
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 (taking place online while COVID-19 arrangement apply) 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.
If you’re starting university in 2020, we’ll be teaching you in different ways to make sure you get the best learning experience possible. You’ll learn through live sessions with teaching staff and have the chance to study independently too, with access to all the online resources you need through our globally available student portal.
We’re planning different scenarios for teaching so we can be flexible. While we’re social distancing, we currently plan to teach your course fully online through a mixture of live interactive sessions and independent learning. This will ensure you’re equipped with the same skills as on campus study, and offer you an engaging learning experience where you can meet and network with your lecturers and fellow students through online platforms. We are also exploring opportunities for face-to-face interactive sessions with smaller groups of students and staff where possible and we can make the appropriate arrangements. You will still be able to access bookable study spaces on campus, and any of the facilities and support services which are open, as well as our extensive online support.
The table below shows current plans for your learning across a typical week, including scheduled live online teaching and an indication of what we hope to teach face to face, where you can make it to campus. While some weeks might look different to this, due to how we schedule classes, the table gives you an idea of what to expect based on the overall number of teaching hours on your course.
You’ll receive final arrangements for your teaching and a full course timetable before you start.
Course delivered fully online while current social distancing measures are in place
Contact time per week per level:
Self-paced learning time
Average hours per week per level:
On demand resources
Average hours per week per level:
Read more about our scenarios for returning to campus and what they might mean for your teaching and learning experience, and how you’ll be able to access student support.
We’re developing our timetable for face to face teaching with current government advice on social distancing to keep you safe. If social distancing requirements are lifted, we’ll start to safely move back towards our usual teaching arrangements with more opportunities for face to face learning. Some learning and support might stay online in this scenario. If more restrictions are put in place, or there is another lockdown, we’ll be prepared to deliver your learning and support fully online, with alternative arrangements made for any required placements. We’ll always give you notice of any changes that we make.
You’ll have a strong support network available to you to make sure you develop all the necessary academic skills you need to do well on your course.
Our support services will mainly be delivered online and you’ll have access to a range of different resources so you can get the help you need, whether you’re studying at home or have the opportunity to come to campus.
You’ll have access to one to one and group sessions for personal learning and academic support from our library and IT teams, and our network of learning experts. Our teams will also be here to offer financial advice, and personal wellbeing, mental health and disability support.
Read our guide to what’s been happening in your subject area recently and more about what to expect this autumn.
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.
We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.
Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.
Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.
At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.