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This module will provide students withÌýcomputational and reproducible research toolsÌýthat are frequently used in social data science. More specifically,Ìýit willÌýfirstÌýintroduce students to how to use RÌýStudio and GitHubÌýtogetherÌýas a toolkit for reproducible social science research, illustrating key features of R packages to manage databases and conduct data analysis.ÌýSecond, studentsÌýwill learn theÌýfundamentals ofÌýfunctionalÌýprogramming,Ìýsuch as familiarity with conditional flow (e.g. if-else conditionals) and creating functions to automate some common tasks for data wrangling andÌývisualisation.ÌýStudents willÌýalsoÌýbe shown how they can use these functions in data science projects to make their project workflow more efficient. Third, students will be introduced the concepts of ethicalÌýWebscraping, as well as learning how to scrap both static and dynamic webpages.ÌýForth,ÌýstudentsÌýwill learn how to build and deploy Shiny apps to create interactive data visualisation. Finally, workingÌýwith databases and dealing with big dataÌýwill be introduced.Ìý
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Therefore, by the end of the course, students should be able to collaborate in reproducible research projects with automated data collection and interactive dissemination of research while managingÌýprojects and collaborating with a source-code repository