University College London

Urban geoscience encompasses the geological aspects of the built environment in the context of construction materials and the underlying bedrock that affects the stability of built structures. In London, the relevance of these aspects are evident. This course introduces students to critical aspects of urban geoscience related to suitability of building materials and construction sites, underground water resource, its contamination and fluctuation and, scope of urban mining using London as an example. The concepts learnt must then be applied to any other expanding city in the world in the same contexts of construction and water resources, maximizing resource recovery, and recycling from urban wastes.

This course introduces students to the practice of management, to what managers do, why they do it, and ways in which they set about doing it. Different management activities and roles are explored from both a practical and theoretical perspective. Key management responsibilities such as strategic thinking, analyzing the business environment, marketing, and motivating self and others are explored alongside a real-life study. This provides opportunities for students to apply theoretical concepts to an actual business case.

In this course, students analyze education policy through the lens of economics. There are discussions of rigorous economic arguments for or against different types of policies. The course also examines the best empirical evidence on these arguments.

In this course, students learn the skills to write Python code to implement statistical and machine learning algorithms that can be applied in a range of contexts. Each week the course covers an aspect of computer coding using examples and exercises that drawn on bioscience contexts. Topics will include: probability, maximum likelihood, Bayes theorem, supervised learning: regression and classification, unsupervised learning: dimensionality reduction and clustering, model evaluation and improvement, reinforcement learning, and neural networks and deep learning.

Evidence-based healthcare has taken root as one of the central pillars of modern medicine. Arguably, the delivery of healthcare based on scientific evidence has never been more important, as we grapple with unexplained variations in practice and spiraling healthcare costs. Despite its widespread importance for rational decision making, the ideal of truly evidence-based healthcare remains elusive. This course is highly engaging and interactive and supports students in developing the necessary skills to be effective and critical practitioners of evidence-based healthcare. Students learn to apply these principles in their studies and in their professional clinical careers. Students also have the opportunity to experience a research environment by meeting researchers in the field and completing a group project under supervision and presenting the results orally.