Data Wrangling

Description

Data comes in a variety of shapes and formats: text documents, images, tables, social network graphs, databases, webpages. Data is used for a variety of uses: archiving, analysis, visualization, communication, and even art. Data wrangling is the process of reshaping data so that it can be more efficiently used. The process can be difficult because it is important to preserve, as much as possible, the relevant information contained in the dataset, while at the same time ensuring an ethical treatment of the data subjects, e.g., protecting people’s security and privacy. Data scientist, thus, need to take careful decisions, and it is estimated that up to 80% of the worktime of a data scientist is spent in cleaning and wrangling data. Learning to do this efficiently, thus, proves to be essential across many discipline and industries.

The course aims to provide the students with the tools to handle different sources of data (csvs, spreadsheets, web pages, apis, …), some target formats (long / wide data frames, packages, …) and a variety of data kinds (dates, numeric, strings, text, …). Wherever possible, the students will work on real-world datasets and ethical facets of data wrangling will be explicitly discussed in class. During the course, R will be the default programming language, and the use of JupyterLab and Rstudio strongly encouraged. Reference to other programming languages, e.g. Julia, will be provided. Peer, group, and class interaction will be explicitly required during the course.