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UNIT TITLE: Data Management CREDIT POINTS: 15 FHEQ LEVEL: 7 UNIT DESIGNATION: Traditional UNIT CODE: COM718 ACADEMIC SCHOOL: Media Technology Delivering School: Media Technology Date validated: September Date last modified: N/A Unit delivery model: CD Max & Min Student No: N/A Arts and Arts and TOTAL STUDENT WORKLOAD Students are required to attend and participate in all the formal scheduled sessions for the unit. Students are also expected to manage their directed learning and independent study in support of the unit. PRE-REQUISITES AND CO-REQUISITES: None UNIT DESCRIPTION The concept of Data Science has now become a driving force within the Digital Economy across many strands of both business and social life. This unit explores a wide variety of the concepts and methodologies associated with the management of databases, how data is explored and mined, and the role of predictive analytics. The aim of the unit is to equip both graduates and professionals with the knowledge and skills required for the global marketplace. It will provide enterprise-level training in Database Management, Data Modelling, Data Warehousing and their applications through research-informed teaching and industry case studies. LEARNING OUTCOMES On successful completion of the unit, students should be able to: Knowledge and Understanding K1 Analyse and evaluate situations and theories and to an appropriate audience. Cognitive Skills C1 Critically evaluate a range of appropriate tools and methodologies for their suitability and justify choices made. Practical and Professional Skills P1 Undertake research into advanced data mining and analytics tools and techniques. Transferable and Key Skills T1 Critically evaluate choices and decisions made. AREAS OF STUDY Data Mining and Knowledge Discovery Concepts and theory of Data Mining (DM) and Knowledge Discovery in Databases (KDD) KDD Process including data exploration, cleansing, pre-processing, mining and evaluation Various data mining methods and algorithms including predictive analytics Practical experience using data mining/machine learning software packages Implementation and testing of a DM application using sample, synthetic and real datasets Data Warehousing and OLAP Theoretical underpinning of data warehousing concepts and architecture ETL (Extraction, Transformation and Loading) process in data warehousing Data warehouse design – star/snowflake schema, fact/dimension tables and data cubes Multi-Dimensional Modelling and On-Line Analytic Processing (OLAP) Querying, reporting and using tools to summarise and discover new patterns Big Data and NoSQL Develop an understanding of the theoretical and technical concepts of big data NoSQL databases and data modelling Techniques to manipulate and manage large-scale datasets Big data processing and analytics Evaluation and selection of appropriate tools to develop big data applications LEARNING AND TEACHING STRATEGY Students will be expected to perform additional individual student led research on each topic. This essential work will inform the basis of the work required for the assignments. The learning approach is based on a series of small activities used to explore the key concepts associated with the unit content, and to place these in the context of the assignment. Presentation of core theoretical concepts will take place in the early stages of delivery, but students will move quickly to an exploration of how these are applied in independent work with later stages focused more on development of the transferable skills. This will include regular reviews to ensure that students and tutor are able to monitor the effectiveness of the work and overall progress. Meanwhile, class exercises will develop confidence in applying key techniques and the soft skills associated with stakeholder relationship management throughout. ASSESSMENT STRATEGY The unit will be assessed by one individual written assignment. The assignment will focus on the critical evaluation of the tools used and researched throughout the unit and their application. The report will be underpinned by the inclusion of a software product developed during the unit. The software product and report also offer the potential for the student to show coherent and substantive evidence to an employer of the modelling, analytical and professional skills acquired. ASSESSMENT AE1 weighting: assessment type: length/duration: online submission: grade marking: anonymous marking: 100% Report 3000 words Yes Yes No Aggregation of marks No departure from standard University regulations. Re-assessment Arrangements Students referred in AE1 will be required to revise and resubmit the Report in light of tutor feedback. Unit Author: Date of version: September 2016