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Enterprise Data Modelling Unit: Progression in Databases and Why More Databases Are Needed

This unit covers Enterprise Data Modelling (EDM) with a focus on database development, exploring the exponential growth of data and the necessity for more databases. The course includes practical application of advanced topics like Data Warehousing, Data Mining, Distributed Databases, XML/NoSQL, and discusses tools like iSQLPlus, Oracle, APEX, SQL Developer, and Weka. The structure comprises four blocks of study, followed by independent research and evaluation. Students benefit from regular feedback, a wide range of topics, practical activities, presentations, tutor support, and guest lectures.

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Enterprise Data Modelling Unit: Progression in Databases and Why More Databases Are Needed

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  1. Enterprise Data Modelling (EDM)Unit Code: SWD603

  2. Unit Progression in Databases Level 4 Introduction to Databases Level 5 Enterprise Database Development Level 6 Enterprise Data Modelling

  3. Why do more databases? • “Everyday, we create 2.5 quintillion bytes of data … • 90% of ALL the data in the world today has been created in the last 2 years alone” • (IBM 2013)

  4. Enterprise Data Modelling 2 Parts 1 3 Report Steps 4 5 Blocks Weeks

  5. Five Weeks • The unit is comprised primarily of four ~5-week blocks of study • The four blocks of study are followed by your own research, application and evaluation of a chosen advanced topic that extends from the blocks

  6. Data Warehousing Data Mining &KDD Distributed Databases XML / NoSQL Databases Four Blocks Enterprise Data Modelling

  7. Three Steps

  8. Two Parts Theory Practice Data Mining Data Warehousing XML DB & NoSQL DB Distributed Databases iSQLPlus Oracle APEX SQL Developer Weka

  9. Student Survey Results (July 2015) where “1” is Strongly Disagree and “5” is Strongly Agree

  10. Best Features of the Unit • Good unit structure and clear approach • Workload spread out evenly across four blocks • Regular feedback and resubmission of draft work • Wide range of topics and tools (e.g. data mining, NoSQL, Big data, data warehousing and OLAP) • Practical activities help work for assessments • Presentations at beginning of each session • Tutor support during class • Guest lectures

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