This programme provides practical, career-orientated training in social science research methods, including research design, data collection and data analysis relating to both qualitative and quantitative modes of inquiry.
Students will have the opportunity to specialise in particular methodologies and to learn more about the application of these methodologies to illuminate important issues and debates in contemporary society.
The programme is designed to provide a fundamental grounding in both quantitative and qualitative research skills, along with the opportunity to specialise in more advanced training in quantitative research, qualitative research or in practical applications of research techniques.
This module offers an introduction to the different styles of social science research as well as guidance and illustrations of how to operationalize research questions and assess them empirically. Students will be shown how to conduct systematic literature searches and how to manage empirical research projects. The module will also explore issues around the ethics of social science research as well as the connection between social science research and policy concerns. It is designed as preparation for undertaking postgraduate research and dissertation work.
This module aims to deepen students' understanding of key debates in social theory and research, providing advanced level teaching for those building upon basic knowledge and undertaking postgraduate research. It is designed to demonstrate and explore how social theory is utilised, critiqued and developed through the pursuit of social science research.
The purpose of this module is to illuminate the theoretical underpinnings of qualitative research. The module will discuss the impact of various theories on the nature and conduct of qualitative research particularly around questions of epistemology and ontology. The role of different types of interviewing in qualitative research will be utilised in order to explore the relationship between theory and methods.
The aim of the module is to provide a comprehensive overview of the theory and practice of measurement and constructing quantitative data in the social sciences. Through lectures and practical exercises, this module will provide students with relevant knowledge of secondary data sources and large datasets, their respective uses and usefulness, and their relevance for the study of contemporary social issues
The module will provide students with an overview of different approaches to qualitative data analysis. It will include introductory training to this skill that includes such techniques as thematic analysis and discourse analysis, as well as computer assisted qualitative data analysis. It will provide the knowledge necessary for the informed use of the qualitative data analysis software package NVivo. The module gives students a base level introduction to the analytical and technical skills in qualitative data analysis appropriate to the production of a Master's dissertation and/or use of CAQDAS software for social science research purposes.
This module provides an introduction to the basics of quantitative data analysis. The module will begin with a brief review of basic univariate and bivariate statistical procedures as well as cover data manipulation techniques. The module is taught through a series of seminars and practical workshops. These two strands are interwoven within each teaching session. Please note that students may be granted an exemption from this module if they have already successfully completed a module that has the equivalent learning outcomes.
This module advances students' confidence and knowledge in the use of SPSS. The module focuses on multivariate regression models, including the appropriate use and awareness of statistical assumptions underlying regression and the testing and refinement of such models.
A dissertation of no more than 15,000 words on a topic relevant to social science research methods training. The thesis will involve either carrying out and reporting on a small social science research project which includes a full and considered description and discussion of the research methods employed or the discussion of a research issue or technique to a level appropriate for publication.
We offer a range of advanced modules in quantitative and qualitative research methods, for example, logistic regression, internet-based research and visual research methods. We also provide specialist modules which reflect the teaching team’s diverse research interests, from the social logic of emotional life to conflict and change in divided societies. Optional modules generally run during the Spring semester and are offered subject to sufficient student demand and staff availability. Students will be able to choose a maximum of three to four option modules (depending on whether they need to complete Quantitative Data Analysis: Foundational). Please note that it is unlikely that all the following modules will be available for 2017/8. Please check with the Programme Director for queries about specific modules.
There is an enormous and increasing amount of data that is collected. Examples include not just traditional data such as sales transactions, but location data (GPS), interactions between people on social network, measurements of sleep patterns, medication being taken, state of health, and much much more.
A key challenge is then to make use of this wealth of data. How can we manage this data, and analyse it to exploit useful information that can guide decision making?
This emerging area goes under the name “Data Science”. There is growing demand for people, “Data Scientists”, who have the skills to manage and analyse enormous amounts of data using a range of techniques such as data mining, statistical techniques, and machine learning.
Data Scientist has been called the “Sexiest job of the 21st century”, and the unique combination of technical skills (stats, data management) and business understanding has been said to make Data Scientists “highly sought after and highly paid”.
The MBusDataSc primary focus is to equip you to become a practitioner, allowing you to meet the needs of industry, and solve the data problems of the world. However, there will also be an alternative path that will focus on preparing students for research in the area (e.g. going on to do a masters by research or PhD).
The proposed degree is inherently multidisciplinary, featuring Information Science and Marketing, which gives the degree a strong business focus; as well as contributions from Computer Science and from Statistics.
Once you have completed the MBusDataSc you will have developed an advanced knowledge of data science. You will understand how data analysis can be used in business, including being able to identify opportunities to use data, be aware of ethical and privacy issues and possible mitigations, and be able to select appropriate means of presenting the results of analysis. You will be able to select and apply techniques to manage and analyse large collections of data.
The programme of study shall consist of seven 20 point taught papers together with a 40 point applied project or research project. Papers are either taught in semester one, semester two or are full-year papers.
You must complete:
Plus one of the following project papers
BSNS 580 - Research Project (for students who may wish to progress to PhD study)
The University of Otago coursework masters programmes provide you with an opportunity to specialise in advanced study with a focus on either applied practical or academic research.
Graduates of the MBusDataSc will gain skills in three areas: those relating to the business and organisational context, those relating to computing technologies for managing data, and those relating to data analysis techniques.
As a graduate of the MBusDataSc you should be able to:
These postgraduate programmes aim to create highly sought-after researchers who are ready to apply their advanced knowledge and practical skills in the workplace or on further research.
You will learn how to collect, analyse and interpret social data and become skilled in interview techniques, surveys, problem-solving, communication skills and the latest industry software.
Students examine issues from across the social sciences and are introduced to both quantitative and qualitative research methods before having the option of specialising.
During the programme, you will research real world issues such as evaluating local health care services, predicting voting behaviour during elections or researching the impact of Hull’s year as the 2017 UK City of Culture on local people.
Taught by experienced researchers who are experts in their fields, the interesting and varied curriculum will be delivered through an enquiry-based approach to teaching including small-group work, tutorials, workshops and independent study.
It was designed with input from industry experts, former students and leading academics to ensure that it means the demands of the modern social research industry.
Students will be provided with a high level of academic support across the programme and all modules will be taught on one specific day (currently Thursdays) to accommodate part-time and working students.
There are four variants:
MSc in Social Research
Semester 1 (PGCert)
Semester 2 (PGDip)
Summer period (Masters)
MSc in Social Research with Quantitative Methods
Semester 1 (PGCert)
Semester 2 (PGDip)
Summer period (Masters)
MA in Social Research with Qualitative Methods
Semester 1 (PGCert)
Semester 2 (PGDip)
Summer period (Masters)
MSc in Social Research (Doctoral Training Pathway)
Semester 1 (PGCert)
Semester 2 (PGDip)
Summer period (Masters)
* All modules are subject to availability.
These programmes are an ideal route for those aiming for research careers in the public, voluntary or private sectors, including would-be senior civil servants keen to take advantage of the Government’s Fast Stream scheme to find the leaders of the future.
It equips students with practical skills and experience for a wide range of organisations including research agencies, charities, independent organisations, trade unions, pressure groups and lobby groups.
The programmes also offer continuing professional development for those already working as researchers and who want to advance their careers. It is also an excellent training programme for those wishing to progress to PhD level study.
The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.
The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.
The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.
The MSc in Data Science provides you with these skills.
Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.
As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files.
The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice.
The programme includes:
You will study the following core modules:
You will also choose from an anually approved list of modules which may include:
Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government.
The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing.
This could lead to a variety of potential jobs including:
Find out more about employability at Goldsmiths.
Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.
Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.
Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.
You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.
Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments.
The course covers three main areas:
You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques.
Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available.
With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.
You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules, subject to availability, allowing you to focus on particular areas of interest.
*Option modules are indicative and may change, depending on timetabling and staff availability.
A wide variety of organisations draw upon data analytics specialists to help produce valuable information for decision-making, for example commodity price forecasting, customer intelligence, clinical trials, R&D and many other areas utilising large amounts of data.
Graduates are able to choose from a range of private, governmental and academic roles, depending on their personal interests. Some of our full-time students find a full-time job and switch to part-time study in the middle of the course.
Graduate destinations include:
This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.
Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.
The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.
City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.
The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.
Accredited by BCS, The Chartered Institute for IT for the purposes of fully meeting the further learning academic requirement for registration as a Chartered IT Professional, and on behalf of the Science Council for the purposes of partially meeting the academic requirement for registration as a Chartered Scientist and a Chartered Engineer.
MSc Data Science students can participate in our professional internships programme, which is supported by the Professional Liaison Unit. This will enable you to undertake your MSc project in an industrial or research internship over an extended period compared to regular projects. For example, the individual project can be carried out as a 6-month internship in one of the companies with which City has a long-standing relationship and history of collaboration in the big data and data science area.
Examples of company placements internships taken by our Data Science students in the recent past include: Google, SagePay, Reward, Black Swan.
The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.
Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.
Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.
In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.
We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.
The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.
From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.
City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.
After successful completion of the course you may wish to consider a PhD degree in Computing.
This one year MSc programme in Statistics and Computational Finance aims to train students to work as professional statisticians, not only at the interface between statistics and finance, but to provide skills applicable in sociology, health science, medical science, biology, and other scientific areas where data analysis is needed.
The emphasis of the programme is on data analysis. It equips students with contemporary statistical ideas and methodologies as well as advanced knowledge, which will make students very competitive to industry, academic and governmental institutions. There are excellent career prospects for employment in industry and the public sector for our graduates. An MSc degree in Statistics and Computational Finance provides attractive employment opportunities in financial industries, government, consultancy companies, research centres, and other industries where data analysis is needed. Students with an interest in academic work may also decide to continue on a PhD programme in Statistics or a related field, for which the MSc in Statistics and Computational Finance provides a sound foundation.
There are excellent career prospects for students with a background in statistics and data analysis. The programme is designed to equip students with contemporary statistical ideas and methodologies which makes our students very competitive when seeking employment in industry and governmental institutions, as well as in academic careers. The skills taught are applicable in sociology, health science, medical science, biology and other related disciplines where data analysis is needed.
Recent destinations of graduates from the MSc in Statistics and Computational Finance have included:
-PhD in the Department of Mathematics at the University of York (Non-parametric modelling in high dimensional data analysis)
-PhD at Florida State University
-Modelling Analyst (automotive data provider)
-Graduate Technical Analyst (HSBC)
-Research and Development in a Property and Casualty Insurance company, specialising in catastrophe insurance
-Mainframe Software Solution Sales in a major IT brand
-Data Analyst in a health data company
-Trainee Chartered Accountant
To achieve an MSc degree students must complete modules to the value of 180 credits, including 100 credits of core taught modules, 20 credits chosen among the optional taught modules, and a 60-credit dissertation.