The goals of this course are to get familiar with data science, with spatial data in data science problem, with techniques and tricks to using R for solving smaller problems, and with programming R to solve larger problems. The final assignment is writing an R package for solving a particular spatial data science problem that passes all checks cleanly, and that contains one or more vignettes illustrating the
problem(s) solved. The grade is composed of the grade for the final assignment, and a grade for active participation (weekly assignments).
Carrying out a proper scientific data analysis exercise involves not only understanding appropriate statistical methods, but also understanding the data, of the domain from which the data and the research question originate, and familiarity with tools that are needed or that still need to be developed to carry out the analysis. Ideally, the analysis should be carried out such that the report does not only give full insight in all details of the analysis, but also makes it relatively effortless to reproduce or modify the complete analysis, and by that recreate the report itself, or modify it. The combinations of all these skills can be called data science.
Spatial data science is a flavor of data science where spatial locations play a role, not only to hold records togehter, but also to compute and analyse distances, consider spatial autocorrelation, or compute spatial relationships between features with different geometry types, such as distances from points to linestrings. Quite often, temporal considerations play a role too when considering spatial properties.
R is a language and environment for statistical computing that plays a large role in data science, because it is open source, it is easily extendible, and it used a lot by non-computer programmers to carry out programming tasks.
For the course organization, see https://github.com/edzer/sdswr