Sociology with Data Science - BSc (Hons)

University of Essex

Want to study sociological classics? Wish to read and interpret original texts by Marx, Durkheim and Weber? Then study a selection of the contemporary writers who followed? We look at classic and modern thinkers, carrying their ideas into new contexts and inverting approaches to social understanding.

With research methods rapidly changing in response to the large-scale generation of data within society, Social Science needs to ensure it is engaged with new digital methods to both benefit from them, and to shape them. In this module students will learn to combine their growing knowledge about society, social processes and research design, with powerful tools to both draw on and analyse the vast amounts and forms of new social data in a way that is With research methods rapidly changing in response to the large-scale generation of data within society, Social Science needs to ensure it is engaged with new digital methods to both benefit from them, and to shape them. In this module students will learn to combine their growing knowledge about society, social processes and research design, with powerful tools to both draw on and analyse the vast amounts and forms of new social data in a way that is critical, ethical and valuable.

How does stratification lead to inequality in education? Is there social mobility between generations? Do early life experiences influence your later choices and decisions? Examine sociological empirical research on class, gender, and racial inequalities across the life course. Engage with the evidence to formulate your own research questions and hypotheses.

This module provides 2nd year students with the tools to put into practice what they have learned in their first year methods module(s) using computer software to analyse data, both qualitative and quantitative. With these foundational skills, students can then specialise in the spring term by choosing either qualitative (SC203) or quantitative-based (SC208) methods modules.

This module provides a broad overview of health systems in the UK and abroad, and a discussion of public health and how medical sociology informs public health. Topics covered will include, but will not be limited to: - the design and history of the UK medical system - the role of professional organizations in medicine and how doctors are socialized into their roles - how patients experience illness, health policy and health equity, medical technology and digital health - medical advocacy and activism - mental health policy and treatment - mental illness, crime and the prison system - disability - reproductive health - alcohol and drug use - the anti-vaccination movement - STIs, HIV and AIDs - global pandemics

This is a co-curricular module carrying zero credits but is compulsory. Upon the completion of the module, each Sociology student will have developed an understanding of their skills, interests, and goals and how these can be implemented to address the societal challenges of the future.

Understand how different statistical and experimental methods can be used to answer questions about political phenomena. You evaluate the assumptions of standard statistical tests and the linear regression model, consider alternatives to those, and learn about causal inference.

Many undergraduates in the Department of Sociology at Essex carry out a research project in the final year of their degree. The third-year project offers you the opportunity to focus on a topic of your choice that relates broadly to your degree course. You will find that this can be an extremely worthwhile learning experience. Carrying out a project improves your employability skills, and can be a springboard to postgraduate study. Note: you must take this module in order to qualify for an AQM (Q-Step) degree.

As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.

This module provides an introduction to machine learning techniques, ranging from supervised learning, unsupervised learning, and deep learning. Also included are the evaluation metrics and procedures of various learning methods. The module focuses on classification and regression learning tasks and their applications to real-world problems.

This is a co-curricular module carrying zero credits but is compulsory. Upon the completion of the module, each Sociology student will have developed an understanding of their skills, interests, and goals and how these can be implemented to address the societal challenges of the future.