Advanced Methods in Applied Statistics
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 science, relevant to the specific graduate research topics and interests of the enrolled students.
MSc Programme in Physics
MSc Programme in Environmental Science
Be familiar with some machine learning algorithms and multivariate analysis techniques
Minimization techniques using Markov Chain Monte Carlo and numerical methods
Maximum Likelihood fitting
Construction of Confidence Intervals
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
- 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.
Academic qualifications equivalent to a BSc degree is recommended.
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.
- 7,5 ECTS
- Type of assessment
Continuous assessmentWritten assignment, 28 hoursAssessment will be based on:
- An in-class short oral presentation (10%)
- Graded problem set(s) 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)
- Practical exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- no restriction
- Study Board of Physics, Chemistry and Nanoscience
- The Niels Bohr Institute
- Faculty of Science
- D. Jason Koskinen (8-6e72766e6c7168714371656c316e7831676e)
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Courseinformation of students