The MSc in Data Analytics is designed for students with a numerate background (for example a first degree in Mathematics, Economics, Accounting, Psychology, Physics or Chemistry) as well as graduates already working in industry. The programme will enable you to utilise and apply your previous academic experience to gain the skills required to work with the large quantities of data that need to be analysed in the modern world.
What is Data Analytics?
Around 200 million tweets are sent per day. Google receives over 2.4 million search requests per minute. The UK’s Department of Health plan to sequence 100,000 genomes, each of which generates 200 GB for data. Walmart’s database contains over 2.5 petabytes of data from the retailer’s 1 million customer transactions per hour. Who will analyse all this data?
Being able to quickly and efficiently analyse large amounts of electronic data is becoming increasingly important for a wide range of org anisations. Huge amounts of data are currently available and the volume being produced is growing rapidly.
Data is generated from a wide range of sources including medicine, use of social media, scientific experiments and sensor networks. This data exists in a variety of formats ranging from structured (e.g. spreadsheets and sensor data) to unstructured (e.g. text, images, video and speech). Deriving information from this data has become one of the key challenges within Computer Science.
Data Analytics focuses on managing vast amounts of information and transforming it into actionable knowledge. The programme teaches the key skills that are required to carry out practical analysis of the types of data sets that need to be interpreted in the modern world. The types of data sets encountered include large data sets as well as structured and unstructured data. The programme makes use of techniques developed within a range of disciplines, including computer science, artificial intelligence, mathematics and statistics.
Why Data Analytics at Sheffield?
Be in demand - our course has been developed to meet skills gaps identified by industry
Gain the specific skills increasingly valued by employers
Access to a dedicated employability team to help increase your employment prospects
Teaching informed by researchers working in relevant areas such as Machine Learning and Natural Language Processing
The Department of Computer Science is 5th in the UK for Research Excellence (REF 2014)
The course covers key techniques for analysing and interpreting data. It is taught collaboratively by two departments - Computer Science, and Mathematics and Statistics.
The Department of Computer Science manages the course and teaches a range of topics, including the Python programming language. Modules in Machine Learning show how information can be derived from data using statistical learning and how these approaches can be applied on large scale using open source technologies. Modules in Natural Language Processing introduce techniques for analysing unstructured data. A module in mathematics introduces key statistical concepts and the R programming language, while another covers topics about the handling and governance of digital information. A team project provides the opportunity to apply techniques learned in other modules to an industrially relevant problem.
There are also options to study modules on parallel computing (including GPUs and CUDA) and computer security. You will have the opportunity to put these techniques into practise with the data analytics project in which you can explore a problem of your own choosing in depth. Projects are carried out in collaboration with providers of data (either internal or external) and completed over the summer.
The MSc Data Analytics consists of taught and research components. The taught component consists of two 15 week semesters from late September until the following June. A research project is then carried out over the summer until mid-September.
Course content
Please note that the course details set out here may change before you start, particularly if you are applying significantly in advance of the course start date.
Core modules
Text Processing
This module focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts. It introduces fundamental concepts and ideas in natural language text processing, covers techniques for handling text corpora and examines representative systems that require the automated processing of large volumes of text.
Machine Learning and Adaptive Intelligence (in Python)
This module will give students a grounding in state of the art algorithms that allow computer systems to learn from data. The module will introduce statistical machine learning, probabilistic modelling and their application to describing real world phenomena.
Statistical Data Science in R
This module introduces a range of statistical and programming techniques and gives practice in their implementation and interpretation using the R programming language. It aims to help students develop the knowledge and experience to select and use appropriate techniques for a variety of problems. The emphasis will be on practical application of techniques and knowledge of their scope rather than development of theoretical underpinnings. Areas to be covered include: exploratory data analysis, simple checks on data, density estimation, simulation, programming and optimization.
Industrial Team Project
This industrially led project aims to provide insights and wider context for the more practical aspects of the taught modules, and to provide students with experience of working in teams to develop a substantial piece of software. Industrial involvement will help ensure the projects remain current and relevant.
Natural Language Processing
This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing. It will cover standard theories, models and algorithms, discussing competing solutions to problems, describing example systems and applications, and highlighting areas of open research.
Scalable Machine Learning
This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelization of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice (e.g. Apache Spark).
Professional Issues
This module aims to promote an awareness of the wider social, legal and ethical issues of computing. It describes the relationship between technological change, society and the law, emphasising the powerful role that computers and computer professionals play in a technological society. It also introduces the legal areas which are specific and relevant to the discipline of computing (eg intellectual property, liability for defective software, computer misuse, etc) and aims to provide an understanding of ethical concepts that are important to computer professionals, and experience of considering ethical dilemmas.
Optional modules
Computer Security and Forensics
This module addresses computer security and forensics issues central to the probity and smooth running of modern industry. The aim of the module is to provide a broad introduction to the topic, which covers the main areas.
Parallel Computing with Graphic Processing Units (GPUs)
This module looks at accelerated computing from multi-core CPUs to GPU accelerators with many TFlops of theoretical performance. It will demonstrate how to write high performance code using hardware such as NVIDIA CUDA GPUs. A key aspect of the module will be understanding what the implications of program code are on the underlying hardware so that it can be optimised.
Students should be aware that there are limited places available on this module.
Dissertation project
Individual dissertation project
This is a research led project based on a topic chosen by the student. The project is completed during the summer, and each student will have a personal academic supervisor to guide them during this period, as well as an external supervisor in the case of industrially led projects. The individual project is examined by a dissertation based on the project work, together with a poster presentation, and there is scope for students to demonstrate their critical skills and topic-related knowledge to a high level.
Careers
Be in demand
McKinsey & Company have projected a global demand for 1.5 million new data scientists. Data from IT Jobs Watch shows a strong demand within the UK, with average salary of £37,500.
The MSc in Data Analytics will equip you with the key skills valued by employers, enabling you to progress rapidly within your chosen profession.
Graduates are in demand across the public and private sector, with potential employment routes including:
Data Scientists
Scientific research
Data Science/Analytics consultancy
Further study, including PhD
What employers are saying
"There is an increasing demand for graduates with the skill sets that the MSc in Data Analytics will deploy not just for Amazon but across the new digital landscape and industry." - Ralf Herbrich, Director of Machine Learning (Amazon)
"The rapid explosion of E-commerce means everyone now has data, so everyone has to manage it and wants to get value from it. The demand for data scientists outstrips the current supply. There is a need for a new breed of Data Scientists- people who have a broader range of Data Analytics experience and skills, with maths one part, and computer science much more important." - Adrian Lingard, CEO (Jaywing PLC)
"There is a severe shortage of graduates with the requisite computational and statistical skills. The MSc in Data Analytics provides the necessary statistical and computational background to ensure that graduates are 'data ready'." - Alfredo Kalaitzis, Data Scientist (Microsoft)
Entry
Academic entry requirements
Applicants are expected to have an upper second class degree, or better, in a subject with a significant mathematical component (e.g. mathematics, economics, accounting, physics, chemistry or engineering).
