Data Analysis for Science and Engineering


Lecturer(s) :

Davison Anthony Christopher
Goldstein Darlene
Morgenthaler Stephan
Panaretos Victor




Postponed until further notice


An overview course intended for scientists and engineers who need to use statistical methods as part of their research, who have already attended a course at the second-year EPFL undergraduate level, and need revision and deepening of their knowledge at a more conceptual level.


This four-credit course is intended for PhD students who need to use statistical ideas and data analysis as part of their research. It is assumed that they have already attended a first course in probability and statistics, at the level of an EPFL second-year course for engineers, and need a broader coverage at a more conceptual level. The course structure is akin to Diggle and Chetwynd (2011), but with different emphases and choices of material.

The course will consist of two classroom hours per week over one semester, plus assigned reading, plus exercises using the statistical package R. Students are expected to submit a problem (which might be a dataset) from their research before the course begins, so that the contents can be tailored to the problems proposed, and the course assessment will be based on a report and presentation in which ideas from the course are applied to the problem.

 1. Introduction:  

 2. Looking at data:  '

 3. Probability revision: Flipped classroom on basic probability (probability distribution, random variates, conditional distributions, limit theorems), based on   assigned reading.

 4. Probability models 1:'

 5. Probability models 2:'

 6. Statistics revision: Flipped classroom on basic statistics (point and interval estimation, testing, likelihood), based on assigned reading.

 7. Experimental design 1:

 8. Experimental design 2:

 9. Experimental design 3:'

10. Statistical models 1:

11. Statistical models 2:'

12. Statistical models 3:

13. Statistical models 4: (Possible topics, to be determined by needs of participants)

14. Statistical models 5: (Possible topics, to be determined by needs of participants)



Diggle, P. J. and Chetwynd, A. G. (2011) Statistics and Scientific Method. Oxford University Press.


Data analysis; statistical methods, scientific method

Learning Prerequisites

Required courses

second-year course in probability/statistics for engineers and/or scientists, reasonable mathematical ability

Assessment methods

Project report/Oral presentation

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German