NFYK15002U Advanced Methods in Applied Statistics
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.
- 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.
- 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.
- 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 counts 40% of the final grade.
The remaining 60% of the grade is based on the elements of the continuous evaluation from the course; student who do not have enough points from the continuous evaluation should contact the lecturer to arrange submission of these elements.
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.