Course overview
In this course, students will explore Big Data concepts, including cloud and Big Data architectures, an overview of Big Data analytics, implementation of Big Data initiatives, and to apply these concepts using an industry standard non-relational database environment. Part A: Big Data Fundamentals - The Data explosion and the evolution of Big Data: definition, characteristics, sources and benefits of Big Data; Challenges with the data explosion: information management and strategy, privacy and security, data analytics; Skills, competencies and roles associated with Big Data. Part B: Big Data Technologies Roadmap - Cloud and Big Data architectures; Non-relational databases: techniques for storing and processing large volumes of structured and unstructured data; Hadoop, MapReduce and the Big Data ecosystem; Information quality and governance of Big Data. Part C: Big Data Analytics Overview - Reasoning, logic, dealing with uncertainty, search, indexing and memory, streams, information, language and sentiment, visualisation of Big Data. Part D: Execution and Implementation of Big Data Initiatives - The business case for Big Data Initiatives, analytics process management, innovation using Big Data. Part E: Future Directions in Big Data.
Course learning outcomes
- Identify appropriate Big Data tools to manage and analyse structured and unstructured data.
- Use the map-reduce approach in, for example, a Hadoop ecosystem.
- Discuss common Big Data analytic techniques and their applications.
- Describe common data storage architectures and determine when a non-relational database architecture should be used.
- Identify, for a business objective, relevant organisational Big Data and develop a business case for effective use of this Big Data.