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Students entering the Information Communications and Technology (ICT) profession need to have skills and knowledge of the various aspects of software development, data storage mechanisms and networking impact organizations that store large volumes of data. Organizations, government and research rely on meaningful data for their decision-making process. The continuous growth of data collection has driven advances in managing and processing of large quantities of data.

This course introduces the fundamentals of Data Analytics. Students will learn to understand the implications of the large amount of Data to be processed, analysed and visualized. The course aims to give the students the knowledge and skills of the history of data collections, the growth in data volume and its impact on hardware and software, the implications on data entry and impact of database of file contents, potential for combining various data sets of different file types, the purpose of cleaning data, and the implication of ethics, privacy, and security of data that may convey sensitive information. The course will use technical hands-on technical practice to work through ways to handle data that is too large covering very technical aspects (R, Hadoop, etc.) and analytical packages, allowing students to compare the benefits of either one. In addition, the course covers identification of patterns and trends in data, together with an understanding of the use of Visualisation to communicate the impact data has on an organisation, enterprise, or science, as well as the relationship to Artificial intelligence, Machine learning, and Data Mining. Students will be assessed based on the findings and presentation of patterns and trends for the data sets.

1. Identify and work with data sets of different file types including database;
2. Summarising the history of data and explaining the implication of very large data sets;
3. Identify implications of ethics, privacy, and security in relation to data sets;
4. Visualise patterns of data sets, and the relationship to AI, ML and Data mining.