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PHY231 Data Analysis I

Contant overview

This course provides the basics of data analysis for the physical sciences, focussing on statistics. The course covers the following topics:

  • Bayesian and frequentist probability
  • Statistical and systematic uncertainties
  • Probability distribution functions (PDFs)
  • Correlation and covariance
  • Error propagation
  • Hypothesis testing
  • Least squares fitting
  • Maximum likelihood method
  • Confidence and credibility

The course is structured with weekly 45 min lectures along with 2 hr exercises in python in the afternoon. The content is strongly based on the book by R. J. Barlow, titled "Statistics"

Content delivery and attendance

Lectures: The lectures will be delivered live at the campus, lived streamed on zoom, with a podcasts recorded for offline. Zoom link: Password: Communicated via email

Exercises: There are two rooms available for exercise classes in python. The places will be decided on a first-come-first-serve basis but is enough space for everyone.

Lectures: Tuesdays 09:00 to 09:45 in Y16 G 05
Exercises: Tuesdays 15:00 to 17:00 in Y36 J 23 und Y36 J 33


The course will be graded 1-6. With 50% of the grade from exercise sheets and another 50% from a test given in the middle of the course. The requirements to pass the course will be to get at least 50% of the marks from the exercise sheets and at least 50% of the marks in the exam.

Midterm test (14th November at 15:00 in Y36 J 23 and Y36 J 33):

 * The midterm test is worth 50% of the overall grade

 * It is based on the first eight lectures.

 * There will be a formula sheet which will be shared beforehand this formula sheet is provided as part of the exam.

 * Apart from the formula sheet, the test is closed book. 

 * A calculator is allowed to be used during the exam (but likely not needed).


Patrick Owen

Martin Andersson / Jose Cuenca Garcia / Sergio Sanchez Cruz


Patrick Owen: Y36 J 22  
Jose Cuenca Garcia Y36 K 38
Sergio Sanchez Cruz: CERN   
Martin Andersson: Y36 J 24