The aim of this course is to introduce students of humanities and social sciences to fundamental concepts of data science and equip them with practical data science skills. In this course, students will be exposed to a wide breadth of data science concepts, at an introductory level and in a gentle pace. The course will cover the theoretical aspects of the fundamental data science methods, as well as practical usage scenarios (i.e., research problems) for these methods in humanities and social sciences. The students will analyze the strengths and limitations of the covered data science methods, and critically reflect on their applicability and potential impact in concrete research problems in humanities and social sciences.

The practical part of the course will be based on the Data Science Stack of the Python programming language – the most widely used programming language among data scientists. Throughout the course, the students will be familiarized with different data analysis building blocks (and corresponding functionality in Python) and encouraged to creatively combine these individual building blocks to address (potentially complex) research questions from the disciplines of humanities and social sciences.

Upon successful completion of the course, students will be able to design and implement a quantitative, data-driven methodological approach for a given research problem in humanities. More concretely, the students will be able to:

• Recognize the aspects of the research problem that can be addressed or answered with quantitative data science methods and discern them from the aspects that can only be subdued to qualitative (i.e., substantive) analysis;

• Obtain existing data (e.g., by scraping public content from the web) and collect the data from scratch (e.g., via surveys or crowdsourcing) for the specific research problem at hand, with privacy and intellectual property considerations in mind;

• Identify the most suitable data analysis approach (or a combination of approaches) for the research problem at hand (e.g., distinguish problems that require descriptive data analysis from those calling for predictive algorithms).