Advanced Methods in Applied Statistics

Course content

The course will offer the practical knowledge and hands-on experience in computational analysis of data in all frontier physics research, with particular relevance for particle physics, astrophysics, and cosmology. The course content is based on statistical methods and does not require a specific or broad physics background. It is therefore applicable for many non-physics disciplines in the physical sciences. Lectures, examples, and exercises will be administered via computer demonstration, mainly using the python coding language.


A subset of the course may focus on the analysis features, but not the the science, relevant to the specific graduate research topics and interests of the enrolled students.


MSc Programme in Physics
MSc Programme in Environmental Science

Learning outcome


  • Be familiar with some machine learning algorithms and multivariate analysis techniques

  • Understand the biases and impacts of various confidence interval methods

  • Minimization techniques using Markov Chain Monte Carlo and numerical methods


  • Maximum Likelihood fitting

  • Construction of Confidence Intervals (Poisson, Feldman-Cousins, a priori and a posteriori p-values, etc.)

  • Apply computational methods to de-noise data and images

  • Code a chi-squared function in the language of the students preference (Python, C/C++, Ruby, JAVA, R, etc)

  • Creation and usage of spline functions

  • Application of Kernel Density Estimators



This course will provide the advanced computational tools for data analysis related to manuscript preparation, thesis writing, and understanding the methodology and statistical relevance of results in journal articles. The students will have enhanced general coding skills useful in the both academia and industry.

Instructor lectures, in-class examples, computer-based exercises, and discussion.

No required literature.


For those looking for additional material, “Statistical Data Analysis” by G. Cowan is an excellent choice.


Class lecture notes and links to scholarly articles will be posted online.

- It is absolutely necessary to have extended knowledge and skill with at least one applicable computer programming language (Python, C/C++, Ruby, R, JAVA, or MatLab) for the course, with a preference for Python or C++. At a minimum, students should have accumulated at least 100 hours of writing, modifying, and debugging code in a single software language. If you have any questions or concerns about the coding competency required, please contact Jason Koskinen.
- The ability and experience to install external software packages, e.g. the MultiNest Bayesian inference package or “emcee” Markov Chain Monte Carlo sampler.
- Completion of “Applied Statistics: From Data to Results”, or equivalent, is strongly encouraged but not strictly required.

It is expected that students bring their own laptops or have access to a computer upon which they can install software to write, compile, and execute code.

There may be an introduction in the 1-2 weeks before the course begins to address software requirements and any additional course logistics.

7,5 ECTS
Type of assessment
Continuous assessment
Written assignment, 28 hours
Assessment will be based on:
- An in-class short oral presentation (10%)
- Graded problem sets and project(s) centering around the coding, implementation, and execution of a statistical method (50%)
- Take home final exam (40%)
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

see learning outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 36
  • Practical exercises
  • 32
  • Project work
  • 36
  • Preparation
  • 102
  • English
  • 206