The increasing availability of data is changing how organisations think about themselves, make decisions, and interact with the world. Data and analytics are helping improve business performance, service operations, expert decision-making, and social outcomes. In this course, students will learn how analytics can support organisational decision-making while also developing a critical understanding of the limitations, risks, and broader societal implications of these technologies.
The course will expose students to real-world examples across domains such as price and demand prediction, product quality control, sports analytics, healthcare analytics, legal analytics, education policy evaluation, and large language models. Students will learn how to apply and interpret analytics tools including linear regression, logistic regression, classification and regression trees, random forests, matching methods, visualization techniques, and large language models.
The course will also cover applications related to Environmental, Social, and Governance (ESG) and Corporate Social Responsibility (CSR), including healthcare service delivery, fair and ethical human resource management, responsible AI use, and policy evaluation for social impact. Students will also be introduced to the use of AI tools, such as NotebookLM and AI-assisted coding, to support learning, exploratory analysis, and rapid prototyping. Through these examples, tools, and structured class debates, students will critically examine the benefits, risks, ethics, and governance of AI and analytics in organisational and societal contexts.
The statistical software R will be used in the course, and class demonstrations will be presented in R Markdown Notebook. Students are encouraged to complete assignments and practice exercises using R Markdown Notebook.