Programme Intended Learning Outcomes
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data-driven solutions to inter-disciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Handle large data processing and analysis with programming skills;
- Extract, analyze and interpret market information to create customers insights and facilitate marketing decisions;
- Practically and effectively apply big data to solve marketing problems and challenges;
- Effectively communicate the ideas and outcomes to stakeholders.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Understand how fintech innovations affect the finance industry;
- Recognize the importance of making use of disruptive technology to construct an ecosystem for the financial sector;
- Apply Data Science and Financial Technology techniques to conduct scientific analyses to obtain financial solutions.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Identify and appraise legal issues and norms in the context of a specific application of data analytics in public and private legal spheres;
- Compare and assess domestic and international legal standards governing personal data protection, big data analytics and artificial intelligence;
- Design and develop legal research to analyse the regulatory environment governing data strategies in different jurisdictions to enhance compliance.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Identify and generate healthcare data needed in Precision Medicine, and apply those data to assist disease prediction, prevention and clinical decision;
- Perform AI modeling and apply it in medical research;
- Solve a practical data science problem in the field of biomedical sciences, medicine or healthcare.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Describe the core concepts and theories of linguistics and its subfields;
- Analyze large linguistic datasets to examine the nature and use of language;
- Present the findings of an empirical study in linguistics to an academic audience;
- Interpret the findings of an empirical study examining a feature of language in a written report.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Identify the core goals of quantitative analysis in social science research;
- Utilize probabilistic and statistical models into social science research;
- Apply the computational tools for text and image analysis.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Acquire the underlying technological principles of AI to define AI-related problems and identify their data- driven solutions;
- Apply specialist tools and techniques to design, analyse, and implement AI systems;
- Identify technical challenges and scientific research issues in AI.
- Acquire basic knowledge of data processing, data science, and big data infrastructure for identifying data- driven solutions to interdisciplinary problems;
- Perform inter-disciplinary data analytic tasks using software and data science related techniques;
- Recognize contemporary issues and challenges in data science technologies;
- Derive knowledge and strategies from data and apply them to inter-disciplinary fields;
- Make informed educational judgment with big data;
- Evaluate educational big data and help decision-makers identify short- and long-term objectives and methodologies to bring about or adopt change;
- Collect and analyze data, and employ measurements or metrics to generate new insights about teaching and learning for improved educational outcomes.