Multi-modal Data Science For Digital Health
Appraise and develop decision-support systems for digital health using real-world cases.
By participating in this course, learners will have the opportunity to benefit from both a comprehensive introduction multi-modal data science in the digital health domain; and access to experts with experience of applying these techniques to solve real-world problems. The innovative 鈥楤ring-Your-Own-Project鈥� approach we propose for module assessment will support learners to apply these skills for their own problems and start building their own professional networks.
Topics
On completion of this module, students are expected to be able to:
- Appraise different types of data science strategies in the context of the digital health domain and its challenges.
- Demonstrate an understanding of machine learning techniques to develop solutions for multi-modal healthcare problems.
- Evaluate the performance of data science techniques within a given business context.
- Visualise and explain the outcomes of data science pipelines for different stakeholders in digital health.
The indicative content covered in this course includes:
- Basic data modelling: identification and selection of features from business data, application of a data science pipeline, evaluation and selection of methodologies.
- Data analysis techniques: computer vision, natural language processing/generation, time-series analysis, suitable algorithms for all cases.
- Context of decision-support systems in digital health domain: typical use-cases and data, evaluating decision-support systems and their outcomes.
- Developing decision-support systems: types of decision-support system, development strategies, alternatives.
The Data Lab
The development of this course has been funded by
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Teaching
10 weeks of teaching/learning activity as follows:
- Recorded Lectures: approximately 1 hour/week in total
- Live Lectures: 2 hours/week
- Tutorial exercises: a range of guided exercises to help participants further explore the principles covered in lectures.
Assessment
- Regular formative quizzes to check your understanding and progress.
- Portfolio assessment (comprising group-work as part of a Bring-Your-Own-Project team and individual assessment of completed exercises).
Independent Study
- Materials and exercises are available online, allowing participants to study flexibly and independently at time and place to fit around existing work and life commitments.
- Online tutor support.
Staff Delivering on This Course
The course team is comprised of academics who have won multiple STAR awards, and have significant expertise in data science for digital health. Guest lectures showcasing real-life case studies will be delivered by industry partners.