***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

Data science often requires the storage and processing of large datasets that are beyond the capacity of standard desktop computing. In this module you will learn about high-performance computing and how it can be deployed effectively to perform computational tasks with heavy demands. You will also study the diverse range of architectures for storing and processing very large datasets. The module will cover the core principles underlying software and hardware designs for handling high-demand computation, as well as modern solutions such as distributed systems, super-computers and cloud computing. You will also learn practical skills in software development for HPC and how to access computing resources made available by major commercial providers.

Pre-requisites: ECMM430 Fundamentals of Data Science
Co-requisites: None.

The aim of this module is to equip you with the necessary skills and knowledge to exploit modern computational resources for data-intensive analysis. You will learn about the core principles of data storage and processing, as well as practical skills in how to manipulate large datasets and utilise highperformance computing resources provided by major commercial suppliers. Given the fast-moving nature of the field, the emphasis will be on underlying principles that apply to all systems, but time will also be spent ensuring that you are familiar with current technologies and tools.

Content will be delivered in an intensive one-week teaching block consisting of lectures and practical work. This will be supplemented by guest lectures from industry practitioners working with highperformance computing systems and big data storage. Self-study and coursework will complete the module teaching activities.

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

Discipline Specific Skills and Knowledge

Personal and Key Transferable / Employment Skills and Knowledge

Topics will include:

• Motivation and introduction to high-performance computing and data architectures.
• Parallel computation, shared-memory multiprocessors and distributed-memory multicomputers, multi-core processors, Graphics Processing Unit (GPU).
• Interconnection networks in high-performance computers: topologies, switching, messaging, routing.
• Parallel processing algorithm and program design.
• Characteristics, challenges and architectural models for data storage.
• Heterogeneity, openness, security, scalability, failure handling, concurrency and transparency, client-server model and its variations, peer-to-peer model, cloud architectures.
• Cloud computing for computation and storage
• Modern tools and technogies
• Handling very large datasets: challenges and opportunities.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Reading list for this module: