Learning outcomes: Teaches an overview of the dissertation journey, helping learners formulate and articulate a topic idea, to formatting and write up their dissertation. Breaking the different elements of a dissertation down, learners will be given examples and strategies to structure and finalise a high-quality dissertation. Learners will be taught how to navigate the different milestones of writing a dissertation with confidence, from choosing the appropriate method to conduct research to interpreting data and presenting their findings as well as exploring the ethical considerations involved in writing a dissertation.

Who should take this learner pathway: This pathway is aimed at all students from undergraduate to postgraduate as they start their studies. Examples in the courses are drawn from across the social, health, and applied sciences so that applicability is assured. We recommend that faculty who are teaching or supervising research programmes assign this pathway to students at the beginning of the course before they start their dissertation.

Learning outcomes: Teaches an overview of handling numbers and how to use them to make evaluations and assessments. Learners will be able to use numerical data to describe research with detail and precision as well as interpret data presented in graphs. Using different sets of data, learners will practice finding statistically significant results, discuss p-values and work with samples to measure and investigate people, organizations and societies.

Who should take this learner pathway: This pathway is aimed at all students from undergraduate to postgraduate across disciplines, particularly the social sciences (i.e. psychology, economics, business studies, sociology, social policy, political science, international relations) and digital humanities. The courses are suitable for anyone who hasn’t worked with numbers or stats for a while and needs a refresher. We recommend that faculty teaching courses that have an element of working with numbers, stats, or data, assign these courses to students early on in their learning as it will help build confidence and foundational skills with number manipulation.

Learning outcomes: Teaches an overview of the research project journey, helping learners formulate and articulate a research idea, and prepare and craft and proposal. Breaking a research project down into a step by step process using examples, advice and strategies, learners will be able to navigate their research project with confidence, also developing non-research specific skills such as planning and presenting.

Who should take this learner pathway: This pathway is great for students at all levels, from undergraduates through to taught postgraduates and even early career researchers. Examples in the courses are drawn from across the social, health, and applied sciences so that applicability is assured. We recommend that faculty who are teaching or supervising research programmes assign this pathway to students at the beginning of the course, before they start their research.

Learning outcomes: Teaches an overview of the text mining landscape and why and how to analyze large amounts of textual data, at scale, using the R programming language. Using practical examples and data sets, learners will gain the knowledge and skills on how R works and perform data management tasks and statistical techniques used in the social sciences.

Who should take this learner pathway: This pathway is aimed at social scientists from masters level and above. It is focused on implementing social research methods in R and analysis of textual datasets. No prior knowledge of the R programming language is required but learners need good knowledge of stats and social research methods.

Learning outcomes: Teaches an overview of the different ways you can collect, analyze and manage research and data online, from choosing the appropriate method to conduct research to interpreting data online. Learners will explore the values and limitations of doing research online and recognize and ethical considerations, look at different sampling strategies and understand how and when to conduct research online in relation to their studies.

Who should take this learner pathway: This pathway is aimed at students working with online data at all levels: from undergraduates through to taught postgraduates, early-career researchers, and even experienced researchers. A basic understanding of social research methodology and statistical analysis software (i.e. Excel, SAS, Stata, SPSS) is required for this pathway.

Learning outcomes: Teaches an overview of the principles, techniques, and tools for presenting data in visually attractive and powerful visualizations. Using available data, learners will be able to determine the most important and relevant content and produce a range of visualizations to portray this in an interesting way. By the end of the learning pathway, learners will be able to present their data in interactive ways using the R programming language.

Who should take this learner pathway: This pathway is aimed at anyone working with data, whether for academic research or in the workplace. It is particularly useful for social science researchers looking to enhance the impact of their research. We recommend that institutions make this pathway available to all students, research staff and faculty.

Learning outcomes: Teaches an overview of the core elements of the Python programming language and how these can feed into social scientific work. Reviewing essential elements of Python programming, learners will be able to extract data and use visualization techniques when conducting social science research.

Who should take this learner pathway: This pathway is aimed at students or researchers working with data who have no prior computing knowledge or for those with experience in other languages looking to switch to use Python. However, learners need a basic understanding of social research methods, stats, and file paths and file management to take this pathway. Faculty/instructors teaching a social science course using Python for statistical analysis may want to assign this pathway to students as preparatory material to class, particularly when they have mixed disciplinary students.

Learning outcomes: Teaches an overview of the basic R commands and data structures for manipulating data. Learners will practice reading data from multiple formats in and out of R and develop skills to clean and manage complex data sets. Learners will also Identify and convert texts into matrices to analyze these and generate inferences using quantitative statistical methods, eventually presenting data in an engaging and visual way.

Who should take this learner pathway: This pathway is more intermediate-level and suitable for masters students and above who have some understanding of what programming is, statistics, and social research methods (such as reliability analysis for summated scales). Faculty/instructors teaching a course that uses R for statistical analysis may want to assign this pathway to students as preparatory material to class, particularly when they have mixed disciplinary students.

Learning outcomes: Teaches an overview of journal publishing, informing researchers how to take advantage of different publishing opportunities, how to ensure their research has the widest impact in the most relevant journal, and how to promote it for the widest reach.

Who should take this learner pathway: This pathway is introductory and aimed at academics and researchers looking to get published in a journal for the first time. No prior publication experience is required. Although the courses are foundational, they also provide new, up-to-date and helpful guidance to more experienced researchers who want to publish their research. We recommend that faculty teaching or supervising PhD students or a research programme assign this pathway to students after they’ve completed their thesis.

Learning outcomes: Teaches an overview of writing and formatting a journal article, giving examples and strategies to structure and finalise a high-quality article. Researchers will be taught how to navigate the peer review process and respond to feedback in order to prepare an article and increase the chances of it getting accepted for publication.

Who should take this learner pathway: This pathway is introductory and aimed at academics and researchers looking to get published in a journal for the first time, but can also be taken by more experienced researchers looking for a refresher. The courses will demonstrate how to put your journal article together and finalizing it, obtaining feedback before publishing the work. We recommend that faculty teaching or supervising PhD students or a research programme assign this pathway to students after they’ve completed their thesis.

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