This course provides an introduction to gender theory and feminist politics from a UK perspective. It explores gender studies as an academic discipline and examines the contribution of feminist movements to civil society. The course examines the influence of debates about sex and gender, identity politics and nature/culture, as well as the division of labour in public and private spheres and the question of women’s rights and empowerment. Exploring gender as a power relation that shapes us all both personally and politically, the course connects theories to activism, traces social change, and discusses the relationship of gender to multiple axes of identity such as sexuality, race and class. The course encourages a critical engagement with the production of Western feminist knowledge throughout. Considering key interventions from queer theory, postcolonial scholarship and critical race studies as well as the impact of globalisation and neoliberalism, it examines the complexity and contradictions that make up multiple feminisms. 
Preparatory Reading

Disch, L., and Hawkesworth, M. E., (eds) (2015) The Oxford Handbook of Feminist Theory. Oxford: Oxford University Press  
	Messerschmidt et al., (eds) (2018) Gender Reckonings: New Social Theory and Research. New York: New York University Press 
 

The dissertation is an independent research project carried out over 8 months which is researched and written under the supervision of a member of academic...

Dissertation in Health Data Science (CHME0021)

In this module you will explore the key principles and concepts of statistics, operational research and machine learning as applied to healthcare. The module...

Principles of Health Data Analytics (CHME0001)

In this module you will explore the key principles and concepts of statistics, operational research and machine learning as applied to healthcare. The module will guide you through the variety of mathematical and statistical techniques commonly used in research to improve the efficiency, productivity and quality of healthcare processes and systems. Additionally, the module will introduce you to techniques for analysing and evaluating the performance of organisations, including predicting demand, planning capacity and monitoring patient flow through a healthcare system (e.g. by minimising waiting times).

At the end of the module you will:

1. Discuss systems and methodologies in relation to current and future thinking around health systems and their analysis, and be able to assess the quality and value of data.

2. Understand the essential elements of data visualisation and be able to design an effective visualisation for complex data.

3. Understand how analysis can support clinical care and operational and commissioning decisions, and be able to apply techniques to data in order to derive measures that inform decision-making.

4. Understand the role of variation in assessing outcome data and identifying divergent outcomes.

5. Understand the potential and limitations of using data and models to predict demand, plan capacity and monitor flow, and be able to use a model to assess different configurations of service.

The module is delivered through web-based distance learning in the UCL Virtual Learning Environment plus a 3-day face-to-face teaching session.

McCandless, D (2012) Information is Beautiful, Collins UK ed.

Steyerberg, E.W. (2009) Clinical Prediction Models, Springer New York

Dua et al. (2014) Machine Learning in Healthcare Informatics, Springer