Data Engineering

COMP40711 Data Engineering 15 Semester 1 Undergraduate Level 4 Sandra Sampaio Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The Harvard Business Review in October 2012 described the role of data scientist as 'the sexiest job of the 21st century'. The 'big data' phenomenon has become part of the vernacular, with the digital universe expected to grow by a factor of 44 from 2009-2020 to a trillion Gigabytes [IDC Digital Universe Study, 2010]. This has led to recognition of a data lifecycle and the need for its systematic management, including both technical and societal issues. Particular focus here is on issues such as data standardisation and data quality, and data analytics (description and prediction) across all application domains. All application areas are witnessing the "data deluge", i.e. the ever growing amount of digital data available as part of day-to-day activities in business, science, education, entertainment, etc. Indeed "Big data" has become part of modern vernacular. Engineering, managing and analysing such data is a key for success of all organisations. In addition to the need to work with huge volumes of data, current applications are also challenged with multi-modal data, including un- and semi-structured data, image and video data, spatial and temporal data, etc. This module will examine the entire data life cycle, including data creation, modelling, acquisition, representation, use, maintenance, preservation and disposal. As the majority of data is stored in databases, the module will examine various database engineering approaches to support data management, including database design, data warehousing, maintenance and analytics. Data standards and data quality will be examined and the challenge of "big datasets" will be considered. <wbr style="color: rgb(0, 0, 0); font-family: Times New Roman; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;" /><ul id="docs-internal-guid-471ae843-7fff-db7e-c539-d130d04bd21f"> <li dir="ltr"> Explain and apply the constituent steps of the data life cycle</li> <li dir="ltr"> Describe data engineering techniques; be able to apply and document large-scale data engineering for a given task, comprising various multimodal data types.</li> <li dir="ltr"> Describe and apply technical, ethical and societal issues related to data engineering, storage, access and maintenance.</li> <li dir="ltr"> Explain and apply the main principles of data analytics/ algorithms, and explain their application to various domains.</li> <li dir="ltr"> Describe relevant standards and best practice in data engineering, analyse shortcomings and identify possible strategies and approaches to overcome them.</li></ul> Analytical skills Problem solving Research Written communication <ul> <li> An overview of the data life cycle</li> <li> Data engineering, modelling and design techniques</li> <li> Data storage and warehousing</li> <li> Data access and maintenance</li> <li> Data analytics application and algorithms</li> <li> Engineering non-traditional data types</li> <li> Data standards and data quality</li></ul> 2 Written assignment (inc essay) 100% Regular coursework, returned marked with feedback Y Lectures 10 Practical classes & workshops 20 120