You will take core courses which provide training in fundamental aspects of applied data science, computation and programming, and quantitative methods. These courses together provide the foundations for the topics covered in the optional courses.
You will also have the opportunity to choose substantive electives, from a range of options both within the Department and across the School, allowing you to tailor the programme to your particular interests. You can choose from courses on social network analysis, quantitative text analysis, causal inference, distributed computing, deep learning, and many others. The programme will culminate in a capstone project, where you will creatively apply the technical skills you have learned to a project of your own design.
(*denotes half unit)
Introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python. The course will also cover the foundations of computer languages, algorithms, functions, variables, object-orientation, scoping, and assignment.
Fundamentals of Social Science Research Design*
Provides a basic knowledge of social research design.
Data for Data Scientists*
Covers the principles of digital methods for storing and structuring data, including data types, relational and non-relational database design, and query languages.
Managing and Visualising Data*
Focuses on data structures and databases, covering methods for storing and structuring data, relational and non-relational databases and query languages. The second part focuses on visualising data, including best practices for visualising univariate, bivariate, graph and other types of data as well as visualising various statistics for predictive analytics and other tasks.
Applied Regression Analysis*
Concerned with deepening the understanding of the generalized linear model and its application to social science data.
Applied Machine Learning for Social Science*
Uses prominent examples from social science research to cover major machine learning tasks including regression, classification, clustering, and dimensionality reduction. Students will learn to apply the algorithms to social data and to validate and evaluate different models.
Machine Learning and Data Mining*
Begins with the classical statistical methodology of linear regression and then builds on this framework to provide an introduction to machine learning and data mining methods from a statistical perspective.
An independent research project of 10,000 words on an approved topic of your choice.
Courses to the value of one unit from a range of options
For the most up-to-date list of optional courses please visit the relevant School Calendar page.
You must note however that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.
You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s Calendar, or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the updated graduate course and programme information page.