MSc Urban Data Science and Analytics

Year 1

(Award available for year: Master of Science)

Learning outcomes

Master of Science

Learning Outcomes:

On completion of the year/programme students should have provided evidence of being able to:

-demonstrate in-depth, specialist knowledge and mastery of techniques relevant to applying data science in the urban and/or transportation contexts;

-develop a detailed understanding, and creative application, of diverse concepts, information, and techniques at the forefront of urban studies and urban planning;

-apply data science methods to observed urban policy challenges, including understanding the process of policy decision-making, working with external stakeholders and citizens where appropriate;

-take a proactive and self-reflective role in working and to develop professional relationships with others in academic and industrial contexts;

-formulate ideas and hypotheses and to develop, implement, and execute plans by which to evaluate these;

-critically and creatively evaluate current issues, research, and advanced scholarship in the discipline.

Transferable (key) skills

Transferable (Key) Skills:

Masters (taught), Postgraduate Diploma and Postgraduate Certificate students will have had the opportunity to acquire the following abilities, as defined in the modules specified for the programme:

-practice data science within the urban and/or transportation context, through creative use of modern programming languages and datasets, to shed light on urban and/or transportation phenomena;

-identify, select, and critically evaluate datasets for analysis of urban and/or transportation phenomena;

-The academic and work-ready skills necessary to undertake a higher research degree and/or for employment in a higher capacity in industry or area of professional practice;

-evaluating their own achievement and that of others;

-self-direction and effective decision making in complex and unpredictable situations, ensuring continual professional development.

Assessment

Assessment:

Achievement for the degree of Master (taught programme) will be assessed by a variety of methods in accordance with the learning outcomes of the modules specified for the year/programme and will include:

-evidencing an ability to conduct independent in-depth enquiry within the discipline;

-demonstrating the ability to draw on a range of perspectives on an area of study, evaluate and critique received opinion, and apply breadth and/or depth of knowledge to a complex challenge in a specialist area;

-demonstrating creativity in the application of data science methods towards the understanding of urban and/or transportation challenges;

-making reasoned judgements whilst understanding the limitations on inferences made in the absence of complete data.

Errors, omissions, failed links etc should be notified to the Catalogue Team