Computer Science

Data science has unlocked exciting possibilities for social scientists through its diverse toolkit, including big data analysis, visualisation, and machine learning models, enabling them to extract valuable insights from their data.  Yet, the success of a data-driven project hinges on data quality. This is where data engineering plays a pivotal role. Professionals must ensure that their acquired data is sufficient and accurate and must be adaptable to handle 'messy data' effectively. A substantial portion of time in data-driven projects (anecdotally 80%) is dedicated to cleaning and pre-processing data, with only 20% said to be devoted to building, evaluating, and deploying machine learning models. Despite the emergence of new AI technologies, which promise to automate many coding tasks, data manipulation is likely to remain an indispensable skill due to the inherent messiness of real-world data. By the end of this course, students will be proficient in producing a website to communicate your collected data and showcase your newly acquired data-wrangling abilities.

This introductory course provides a comprehensive introduction to digital research for students from a range of backgrounds. Through a variety of interactive sessions students develop an understanding of the key principles of Open Science and Scholarship, the importance of reproducibility and methods for managing research projects. The course serves as a platform for students to undertake digitally enabled research projects.

This course cover three important ideas in classical physics – Newton’s Laws of Motion, Newton’s Law of Gravitation and the Wave Equation. After considering analytical solutions to each, students look at computational solutions using the Python programming language (no background in coding is necessary) and touch on ideas such as dynamical systems and chaos. Students also look at solutions in different coordinate systems which give rise to familiar ideas such as Kepler’s laws of planetary motion and the inverse square law but from a first principles approach.

Much of modern machine learning rests upon a range of mathematical methods and many introductory machine learning courses seek to introduce algorithms before ensuring the link with these methods is made. This course offers students an introduction to traditional Machine Learning in a rigorous mathematical fashion. Assuming a familiarity with key results of linear algebra, differential calculus, probability and statistics, the course introduces the key areas of traditional machine learning and seeks to cover the key tools (and theorems) within these areas, and to illustrate these with practical exemplars.