Course summary

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment. This has led to a significant increase in demand for skilled data scientists, and this demand is predicted to further grow. Drawing on this, we have created the Master of Data Science (Digital Humanities), a conversion course that opens up a future in data science even if your first degree is in a non-quantitative subject such as arts and humanities. The course equips you with the skills to process and analyse data, communicate your findings to a wide audience whilst applying this knowledge to practical situations. The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as statistical modelling (in R), computer programming (in Python), machine learning, AI and neural networks. Optional modules allow you to focus on an area of interest. The MDS culminates in the research project, an in-depth investigation into an area of specific interest in which you apply the skills you’ve learned during the course to a research problem in a humanities domain of your choice. Core modules: The Data Science Research Project is a substantial piece of research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills. Critical Perspectives in Data Science develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project. Digital Humanities: Practice and Theory introduces you to contemporary debates on the future of the humanities in an increasingly digital world. You will learn about the most important technical tools for representing and manipulating cultural artefacts in digital form, and how to apply cutting-edge theoretical frameworks and technical tools to practical problems in Digital Humanities. Programming for Data Science uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation. Introduction to Statistics for Data Science focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation. Machine Learning introduces the essential knowledge and skills required in machine learning for data science using the R statistical language. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, and neural networks.