KL7011 - Advanced Databases

You will attend weekly lectures and lab-based seminars to learn a range of topics. The lectures will provide key concepts and ideas, which are then followed by hands-on lab sessions whereby you will develop advanced skills in database and data warehouse management using Oracle database system and a range of other useful tools and methods.

You will be given advice and feedback on your formative assessment (e.g., lab exercises) during the timetabled classes. In addition, the eLP module instance will be used to provide extensive supporting material. Constructive and written feedback on first summative assessment will provide you focused guidance on how to improve your work in the following summative assessment. Specific sessions will provide you with further directed guidance on successful completion of your summative assessments.

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

Knowledge & Understanding:
1. Demonstrate critical understanding of the entire data life cycle and classical database engineering processes and approaches, and non-traditional database systems
2. Demonstrate deep knowledge of key concepts of data warehousing, data analytics, data standards, and data quality

Intellectual / Professional skills & abilities:
3. Critically analyse, select, apply and evaluate advanced data modelling, database design, implementation and manipulation methods, techniques and tools to a complex data management problem and explore, recognise and appreciate opportunities for innovation

4. Appraise, analyse, design, develop and evaluate data warehousing and data analytics solutions and evaluate their environmental and societal impact and minimise their undesirable impact.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Develop critical awareness of the responsibilities of database developer with respect to sustainability, professional, legal, security and ethical issues, become aware of applicable health & safety, diversity, inclusion, cultural, societal and environmental matters as well as undertake risk management and evaluation of commercial risk in relation to data management, individually or as part of a team based on critical review of current literature, systems, developments, and standards.

Formative assessment: lab exercises carried out within seminars will build up to form a basis of the two summative assessments. Feedback will be given during lab sessions.

Summative assessments: two written assignments.
• The 1st coursework assessment will be an individual work and will comprise analysis, design and implementation of a solution to a database management problem using classical and non-traditional methods, techniques and tools and undertaking risk management and evaluation of commercial risk of your solution (60%) and will test MLOs 1, 3 and 5.
• The 2nd coursework assessment will be a team-work and comprise design and implementation of a solution to a data warehousing and data analytics problem (40%), which will test MLOs 2, 4 and 5. Peer-assessment will be used to award each student a grade based on their contributions to the team-work.

Feedback: You will be given detailed feedback on the 1st group assignment clearly identifying both the weaknesses and strong points of the work. As this will be set approximately half-way through the module it will enable the students to identify those areas where they need to focus their efforts on in their 2nd assignment.

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The aim of this module to provide you a deep understanding and appreciation of the entire data life cycle (from creation to disposal), classical database engineering processes and approaches to modelling, design and management of databases. You will be able to examine advanced approaches to data integration and data warehousing including design, maintenance and data analytics. Moreover, you will be able to get an appreciation of non-traditional data types, systems and applications (e.g., NoSQL Databases) and data standards and data quality.

This module includes a combination of methods to support learning, including lectures and lab based seminars/workshops allowing you to put the theory from lectures into practice. Topics will normally be introduced in lectures and explored further through real world examples and practical exercises (helping you develop the knowledge and understanding needed) and guided independent learning activities. You will be encouraged to develop independent self-learning skills to explore further in the field of data science.