Data Access and Regulation

The goal of the course is to provide students with a multidisciplinary overview on data processing. According with this objective, each module is focused on a specific aspect.

First module
The first module is devoted to the key elements of data protection law, and will thus explore principles, rights and duties set by the EU General Data Protection Regulation (GDPR) also in light of the relevant case law of European courts.

Second module
Students are increasingly keen on developing empirical projects that involve some big data application, such as analyses dealing with social media data or a large database. However, even when they possess sufficient knowledge of a general programming language, such as R or Python, they usually lack the practical side of working with unstructured and big data. Furthermore, the frontier is moving fast, and big data mining and modeling tools have quickly become essential in the social scientist's toolkit.
This second module of Data Access and Regulation guides the students to move their first steps into data mining, offering real case studies and exercises to learn (by doing) how to handle big data in their future work. The seminar is designed to introduce students to the various skills that are needed to access the (big) data ocean confidently and, importantly, to self-learn new skills in the future. All activities rely on the R programming language and, thus, students are expected to have a minimal working knowledge in R (basic notions will be refreshed in class).

Third module
The third module introduces students to strategies and technologies that are used to handle, store and access large volumes of research data, with a focus on approaches that enable seamless collaboration and ensure your data are accessible to other researchers in the long term.

Students actively participating in the first module are expected to develop an expertise on the legal status and significance of the right to privacy and data protection, and to become familiar with the current legal framework in Europe and the challenges that the digital technologies have posed to this right over the last decades.

Students actively participating in the second module are expected:
1. [Competence in data analysis] to become proficient in data analysis, learning advanced topics such as data wrangling, code efficiency, and workflow reproducibility.
2. [Data literacy] To understand and critically assess data-related issues arising in applied research problems with big data.
3. [Data mining] To gather handle structured and unstructured data subsequently unfolding the tidying data process.
4. [Coding skills] Ability to develop and debug complex code throughout the data analysis cycle (mining, tidying, analyzing, reporting).
5. [Research and analytical skills] Ability to develop original ideas and elaborate feasible big data designs to test their validity.

The third module will give students an overview of database and other data storage technologies, showing them the strengths and weaknesses of each approach and equipping them to make informed decisions about which technologies and platforms to use in their own future research projects. Students will also be introduced to data management planning documents, which are increasingly commonly required as part of research funding proposals, and show them what considerations should be included in the creation of such a plan.