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[S1] identify, assess, integrate and prepare a variety of data from sources relevant to specific contexts, understanding their limits and benefits for future use, and throughout the data and information life cycle.

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[S2] critique and develop data science experiments, and its related practices, processes, instruments, systems and infrastructures to evaluate specific approaches empirically and ethically.

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[S3] implement appropriate data science methods to discover new relations, make useful predictions and deliver insights into research problems or organizational processes and support decision-making, applying citicality to assure approaches are appropriate to specific contexts .

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[S4] analyse and critically evaluate a wide range of different real-life problem contexts from a systems perspective, explaining and interrogating such systems through various ethical lenses, such as sustainability, inclusivity, fairness, and accountability.

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[S5] demonstrate proficiency with key statistical concepts and knowledge used to describe, explore, analyse, and present data in forms appropriate for communicating findings, insights, ideas and issues clearly and effectively to different audiences/stakeholders.

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Ability to plan an effective communication strategy and to choose appropriate ways to communicate findings, insights, ideas and issues clearly and persuasively to inform/influence different audiences/stakeholders, including through data visualisation. (AC)

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[K1] describe the conceptual underpinnings of Data Science, their development as fields of study, and their cultural, social, political and historical contexts within which they are embedded, locally, nationally and globally.

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[K2] explain the theoretical approaches and research evidence concerning the processes of data and AI, their interaction with, and impact on data practice in specific contexts.

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[K4] explain how data governance, data management, project management and strategic planning can contribute to effective data-related services

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it is usually said that data science involves knowledge of computational methods, statistics + domain knowledge. How is domain knowledge built in the course? (AC)

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understand how to identify the priorities of key stakeholders in an organisation or other context and to communicate persuasively the value data science and AI offer to them. Understand processes of public policy making and organisational strategy to ensure data science insights are used effectively (AC)

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demonstrate proficiency in the application of appropriate statistical methods to data analysis (AC)

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[K3] evaluate and synthesize issues concerning rights, diversity, equity and inclusion in relation to all data-related practices – and those affected by them.