University of Sussex

This course introduces the fundamental principles of economics. Initially, students explore micro-economic issues including the behavior of individuals and firms, their interaction in markets, and the role of government. The course then looks at macroeconomics and examines the determinants of aggregate economic variables, such as national income, inflation, the balance of payments, and the relationships between them. The course also introduces mathematical economics, including linear equations, differential calculus, and discounting.

This course explores concepts of industrial engineering, operations management, materials and processes, product design, quality control, sustainability, and CAD among others. In addition, students examine how these factors have an impact on the manufacturing feasibility of a product. Other topics include tolerancing, process capability, production types, product design specification, rapid manufacturing, cradle-to-cradle design, solid 3D modelling, industrial (ISO and BS) standards, technical drawings, cost estimation, and project planning.

This course provides an overview of the main principles of ecology and conservation. Topics include population ecology (population growth, regulation, species interactions-competition, predation, mutualisms) and community ecology (trophic structures and food webs, biodiversity, ecosystem services). The course also explores macro-ecology, local global patterns of biodiversity, factors controlling species distribution, abundance, and biogeography. Finally applied issues in ecology and conservation are considered, including habitat loss and fragmentation, invasive species, harvesting, and ecological restoration.

This course introduces the basic elements of the nervous system, namely the nerve cell, with an emphasis on the mechanisms that allow information to be communicated between neurons and across networks of nerve cells. It also reviews how sensory information is transmitted into the brain and how motor output is engaged. The course focuses on the psychobiology of influences to behavioral change, namely emotion, motivation, and learning.

This course addresses the role that psychological research can play in understanding and supporting family relationships and educational processes. Topics include theoretical approaches to the study of the family (e.g., family systems theory, life-course approaches, socialization) as well as theoretical perspectives concerning children's learning (e.g., cognitive development, sociomotivational factors). The course emphasizes recent empirical work on the consequences of family functioning for children's development and the specific aspects of children's academic development and socioemotional well-being in schools (e.g., key curriculum areas such as reading and mathematics, special educational needs, mental health, and social and emotional learning).

This course introduces data structures and algorithms for computer scientists. The course explores a number of fundamental data structures, including arrays linked lists, stacks, queues, trees, hash tables, and graphs. These are presented both abstractly, via the notion abstract data types, and concretely in terms of their implementation in an object-oriented framework. The data structures are discussed and analyzed in terms of efficiency of the basic operations they support and their application to program design problems. Consideration is given to important, fundamental algorithms for searching and sorting data.

This course incorporates sociological, criminological, socio-legal, and cultural approaches to the study of capital punishment. Students engage with a "cultures of punishment" perspective on the death penalty, drawing on capital punishment scholars such as David Garland (2010), Austin Sarat (2001), and Franklin Zimring (2003).

This course introduces the practice of sociological/criminological research using different methods. Students are introduced to debates in the social sciences related to research design, epistemology, and ethical issues, while also getting practical experience in key methods for gathering and analyzing qualitative data including interviewing, participant observation, and textual analysis. The philosophy underpinning the course is that research methods cannot be adequately taught in the abstract, and consequently the course emphasizes the practice of social research and its application to sociological and criminological issues.

This course provides an introduction to algorithmic problem solving with a focus on algorithmic thinking, not coding. Searching, sorting and other simple (and intuitive) algorithms are specified and developed. Principles like divide-and-conquer and recursive programming are also explored. To establish correctness, some relevant (propositional and predicate) logic is introduced in an informal style (focusing on logical reasoning principles rather than logical calculi). Finally, the concept of time complexity of an algorithm is presented and asymptotic complexity classes are discussed.