Increasingly, research in all disciplines, from the natural sciences to social sciences and humanities, involves big data. The availability of vast amounts of textual, audio-visual and structured data from digital sources is revolutionising research in the humanities and social sciences. The most advanced scholarship in these areas, currently and in the foreseeable future, relies on the use of sophisticated tools for accessing, processing, analysing and presenting this data.
In this three-week module, we use real-world data to allow you to gain familiarity and experience with some common approaches to handling large datasets. You will learn algorithmic thinking and the general concepts required for data analysis with computers. You will engage with a number of very common programming languages and tools, culminating in a group project based on a real-world dataset, where you extract relevant information from it in an automated fashion, perform some simple analysis, and display the results visually.
The focus of this course is to make working with programming languages and helpful digital tools seem less intimidating and to provide you with the tools to approach a large dataset with an understanding of what expertise and techniques are required for extracting the necessary information for your research. It is set up so that anyone who is interested and motivated should be able to follow it, without requiring prior knowledge or previous programming or statistics experience.
Week 1: Introduction to data acquisition, processing and storage (algorithmic thinking, command line interfaces and Python).
Week 2: Introduction to data analysis and presentation (R, LaTeX) and a project incorporating all topics thus far.
Week 3: A real-world dataset is provided. Students will formulate a research question, (pre)process the data, analyse it and present the results to their peers.
The most important goal of this course is to teach you how to deal with digital data in practice. As such, at least 60% of your grade will be based on the process and final presentation of the third-week group project. A smaller portion of the grade will be based on motivation and participation, and skill in the covered topics. The focus will lie on teaching you how to work with data, and we do not expect you to be a programming wizard by the end of the course.
The formal requirements for this course are that you have successfully completed 60 ECTS with a GPA of 3.0 or higher, have fulfilled your breadth requirement and have completed a methods, statistics or mathematics course (this includes methods for humanities). If you do not meet these requirements but are motivated and interested in the material, we nevertheless encourage you to apply through a short 200-300 word motivation. If you are unsure whether you should take this course or have any other questions, please do not hesitate to contact me at firstname.lastname@example.org.