Course overview
The aim of this course is to interoperate with cloud based information systems in order to extract meaningful data for analysis and publish visualisations. The aim of this course is topics covered in this course include: overview of big data analytics; characteristics of big data; analysis flow for big data; big data manipulation techniques: batch analytics (Hadoop Map-Reduce), real-time analytics, interactive querying; big data languages/tools: Pig, Oozie, Spark, Strom, Hive; big data search: Solr, Elasticsearch; big data applications (e.g. recommendation systems, time series analysis, text analytics); and web framework: Django.
Course learning outcomes
- Analyse how data analytics on big data can be used within an organisation to improve current processes and decision making, and to enhance the organisation's capabilities or products.
- Describe the data analytics lifecycle for big data and apply it to constrained problems in big data analytics.
- Select an appropriate big data analytics tool and apply it to a problem involving big data.
- Communicate appropriately with professional colleagues through visualisation and report.
Degree list
The following degrees include this course