|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Report on practical data Practical assignment and report 4000 word report and accompanying source code and data.||50%|
|Semester Exam||2 Hours Written examination||50%|
|Supplementary Assessment||Report on practical data 4000 word report and accompanying source code and data. Students must resit failed examination and/or resubmission of failed/non-submitted assignment work||50%|
|Supplementary Exam||2 Hours Written examination Students must resit failed examination and/or resubmission of failed/non-submitted assignment work.||50%|
On successful completion of this module students should be able to:
Design and implement a NoSQL database with one or more data models
Design, perform, visualise and report exploration and analysis of data-set.
Identify and evaluate the essential concepts behind a variety of NoSQL data models, including key-value, document oriented and graph data models.
Identify potential security, ethics and data management issues raised by the use of computerised data storage and processing and suggest mitigating strategies.
In order to model the wide variety of phenomena that modern data analysts are expected to cover it is important to be able to understand a wide variety of different data storage methods and data models. This module will cover the essential concepts behind modern database models known as NoSQL. We will cover the process of data analytics from initial data modelling through storage, clearing, retrieval, processing, visualising and analysis of a variety of different data models. In addition we will cover technical, legal and ethical issues associated with data collection and storage.
This module teaches practical data handling, preparation and storage techniques.
This module covers technical, legal and ethical issues associated with data collection and storage.
2. Modelling, securing and processing of data. Designing a NoSQL data model, implementing the model and querying the resulting NoSQL database.
3. Data analytics, visualisation and data mining.
4. Vulnerabilities, procedural and technical factors, threat analysis and mitigation.
5. Choosing the 'right' data management system. Evaluating alternative data management systems in terms of data domain, model and project requirements.
|Skills Type||Skills details|
|Application of Number||Inherent to subject|
|Improving own Learning and Performance||Inherent to subject|
|Information Technology||Technical skills related to applying emerging data management systems to problems involving massive volumes of data and high transaction rates.|
|Personal Development and Career planning||Encourages students to see roles in subject for career and personal development|
|Problem solving||Inherent to subject|
|Research skills||Inherent to subject|
|Subject Specific Skills||Technical skills related to applying emerging data management systems to problems involving massive volumes of data and high transaction rates.|
This module is at CQFW Level 7