This unit focuses on methods and techniques to take into consideration the human elements in data science. Humans can act as both sources of data and its interpreters, introducing a range of complexities with regards to analysis. How do we account for the unreliability in data collected from humans? What can be done to address the subjects¿ concerns about their data? How can we create visualisations that facilitate understanding of the main findings? What are the limitations of any predictions? The ability to consider human factors is essential in any loop that involves people gathering, storing, or interpreting data for decision making. On completion of this unit, students will be able to identify and analyse the human factors in the data analytics loop, and will be able to derive solutions for the challenges that arise.
lectures, laboratories, project work
through semester assessment (40%), final exam (60%)
Basic statistics, database management, and programming.