| The goal of this course is to give participants a
first gentle introduction and solid conceptual grounding in what has
been called ‘data science’, i.e. experimental work that is data-driven
and empirical.
The focus is on methodology, defining an experimental
protocol, devising hypotheses, thinking about measuring success, but
also on more practical approaches like basic machine learning methods
(both supervised and unsupervised)
and the introduction to popular tools. The course also
demonstrates some practical applications of the techniques shown, and
deepens the students' skills via practical exercises. |