Data Science MSc with Integrated Placement (15 months/24 months)

Course content

What will you study on the MSc Data Science with Integrated Placement (15 months/24 months)?

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.

As part of this course, you can do an optional three month or one year industry placement. Programmes with integral placements give you the opportunity to apply the skills you have learned throughout your studies in a practical environment. You will be earning a full time salary and will learn skills that can't be taught in a classroom at University. During the placement, you will be able to gain further insight into industrial practice that you can take forward into your individual project and into your future career.

You will undertake your placement after completing your taught modules and project. During the placement, you will be assigned an academic supervisor, who will maintain direct contact with you. You will be able to access online materials at the University as well as other physical resources as appropriate. You will maintain a placement log for the day-to-day activities and submit a final report once the placement comes to an end.

Although the placement is not guaranteed, the University maintains links with a wide network of organisations who offer placement opportunities. The University will also provide you with full support to help you secure a placement, from job application to the interview.

In order to qualify for the placement period you must have passed all modules in the semesters preceding the placement.

You can find more information about this course in the programme specification. Please note that optional modules may not run, due to student numbers or staff availability. 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.