Applied Quantitative Methods

Manchester Metropolitan University
UK
Provided by: Into Global

Progression Summary

Based at the Manchester Metropolitan University Q-Step Centre in the Department of Sociology, this MSc approaches the study of quantitative methods in an innovative way. Right now, there is a demand for graduates with quantitative skills, and it is a demand our students are equipped to meet. Students studying on this MSc will be ready to pursue a career involving quantitative research skills within a diverse range of organisations. Students will also be prepared for doctoral study, which requires advanced skills on quantitative methods recognised by the Economic and Social Research Council (ESRC) and other research councils. The course focuses on the development and application of key quantitative methods and analytical techniques. It focuses on the use of quantitative methods within real world contexts and a key aim is to develop graduates' career-ready skills in this field. This course also offers a unique opportunity to develop specialism in key research fields informed by the specialisms within the various centres within the Department. All of these specialisms can be taken with the addition of a placement year. Depending on your individual programme of study you will either graduate with: - MSc Applied Quantitative Methods - MSc Applied Quantitative Methods (Impact Evaluation) - MSc Applied Quantitative Methods (Big Data Analytics) - MSc Applied Quantitative Methods (Longitudinal Data Modelling) - MSc Applied Quantitative Methods (Participatory and Creative Research) All of these specialisms can be taken with the addition of a placement year. **Features and Benefits** - Teaching Excellence: This MSc is based in the Manchester Met Q-Step Centre, which has been recognised nationally as a centre for excellence in the teaching and training of quantitative methods. It has pioneered a range of innovative approaches to the teaching of quantitative methods (QM) and all its staff are active QM researchers. - Upskilling Opportunity: If you are new to QM this is the course for you, as it is designed for entrants who have no previous knowledge of QM. Through studying the foundations of Quantitative Methods, you will go from no quantitative skills to being able to do a simple regression in two weeks. This MSc also offers the opportunity to upskill your quantitative skills by studying individual units as short courses/CPD, with or without credit. - Career-focused: this course looks outwards to the workplace and prepares you for a range of careers using QM. Through completion of a successful application students have the opportunity to do a research placement with one of our external partners. Also, if you choose to, you have the opportunity to develop an understanding of core data management skills required by data scientists. - Specialisation: this course offers specialist training in QM, including multi-level modelling, multiple regression, impact evaluation, longitudinal data modelling and big data analysis. This course allows you to develop research specialism by conducting your dissertation on a specialised research field connected to core research centres in the department. - Research informed teaching: The course is taught by a group of staff who are active researchers. They are involved in a number of large national and international research projects funded by key funding agencies including European Commission, Economic and Social Research Council, Nuffield Foundation, Department for Education. - Gaining work-ready skills through placement: If successful with your application, you will have the opportunity to complete a research placement with one of our external partners, supporting you to develop work-ready skills and enhance your employability after graduation. - Work-based data-driven learning opportunities: Through a placement, you will get opportunities to work with real life data and learn valuable experience on how businesses make decision based on data.

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Start Date

09/2023
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Tuition fee

Not found

Full-time

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