You will take five compulsory courses, including a Capstone Project. You will also select optional courses to the value of one full unit from a list of options.
The Capstone Project will provide you with the opportunity to study in depth a topic of specific interest. The topic may be identified from a list supplied by the Department or may be proposed by you. The topic will normally relate to a specific data source or sources and will require the use of data science skills learnt on the programme. The topic for a Capstone Project will be similar to that for the kinds of data-based issues faced in practice by private or public sector organisations. The focus is likely to be practical and there may be the opportunity to liaise with such an organisation during the project to ensure the project has practical relevance. A Capstone Project will be more academic; it will refer to a research literature and address a research question, building on that literature and using the data source(s).
(* denotes a 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.
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.
Data Analysis and Statistical Methods*
This course will provide an introduction to methods of statistics and data analysis. The statistical software R will constitute an integral part of the course, providing hands-on experience of data analysis.
Machine Learning and Data Mining*
Begins with the classical statistical methodology of linear regression and then build on this framework to provide an introduction to machine learning and data mining methods from a statistical perspective.
An in-depth study on an approved topic of your choice.
Optional courses to the value of one full unit from an approved list
^ students who can demonstrate equivalent prior knowledge of this course, via transcripts of prior qualifications, may skip this course and take a further half unit of options from the options list
To find 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 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.