This is a new masters course since 2014.
This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reasoning. There is space to discuss your ongoing experiments.
The course teaches the acquisition of a methodology of designing experiments for optimal quality of the results and of the number of experiments. Specifically the objectives are:
- To transfer to the student the conceptual basis for designing, performing and analyzing statistical design of experiments
- To let the student understand the methodology of response surface, with the mathematical concepts that allow the evaluation and the optimization of a matrix of experiments
- To develop a principle of know-how to evaluate, optimize and analyze design of experiments
- To develop conceptual understanding of the design of experiments that allows the PhD student to collaborate with statisticians
This course teaches the basic techniques and practical skills required
to make sense out of a variety of data, with the help of the most
acclaimed software tools in the Data Science world: pandas,
scikit-learn, Spark, TensorFlow, etc.