NFYK15002U Advanced Methods in Applied Statistics

Volume 2024/2025

MSc Programme in Environmental Science
MSc Programme in Physics


The course will offer the practical knowledge and hands-on experience in computational analysis of data in 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.


Interested Ph.D. students and non-physics M.Sc. students in the Physical Sciences are very welcome to enroll.

Learning Outcome


  • Be familiar with a supervised machine learning algorithm and multivariate analysis technique, e.g. Boosted Decision Trees.
  • Parameter estimation and uncertainty estimation using likelihood and Bayesian techniques
  • Minimization techniques using Markov Chain Monte Carlo and numerical methods (minimizers)



  • Maximum Likelihood fitting
  • Construction of confidence intervals and contours
  • 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
  • Inputing and processing data from both ASCII-readable files as well as internet data scraping.


This course will help students develop the computational tools, software development, and use of statistical software packages for data analysis.  The data analysis techniques are reinforced through assignments, which are important for 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.  Students will develop their own software solutions and tools and strengthen their independent problem solving skills.

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++/C11, Ruby, R, Rust, JAVA, Julia, or MatLab) for the course. At a minimum, students should have accumulated at least 100 hours of writing, modifying, and debugging code in at least one of the aforementioned software languages. A background with only graphical coding languages (such as Visual Basic or LabView) will unfortunately not satisfy the coding requirements for this course. 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. a 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.
Instructor lectures, in-class examples, computer-based exercises, and discussion.
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.
Example solution code will only be provided for a small subset of in-class exercises. As such, students should be prepared to develop and code their own, or collaborate with classmates, solutions in order to solve the problems.
  • Category
  • Hours
  • Lectures
  • 36
  • Preparation
  • 90
  • Practical exercises
  • 32
  • Project work
  • 36
  • Exam
  • 12
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Written assignment, 28 hours
Type of assessment details
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

The re-exam includes a new 28-hour take home test which constitutes 40% of the weighted sum used to calculate the total re-exam grade. The remaining 60% can be re-used from the continuous evaluation during the course, or the student can choose to submit 3 new projects (as defined and described during the course) 2 weeks prior to the re-exam date. All parts are assessed together.

Criteria for exam assesment

For a 12, a student must display mastery of an orally presented topic including accurate answers to follow-up questions, in addition to the contributions from graded problems sets, project(s), and take-home exam. The final assessment will be a total of all components.