NFYK15002U Advanced Methods in Applied Statistics
MSc Programme in Physics
MSc Programme in Environmental Science
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.
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.
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.
- 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.
There may be an introduction in the 1-2 weeks before the course begins to address software requirements and any additional course logistics.
- Practical exercises
- Project work
- 7,5 ECTS
- Type of assessment
- Continuous assessmentWritten assignment, 28 hoursAssessment 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
Take-home exam in coordination with the course responsible, 28 hours.
Criteria for exam assesment
see learning outcome