Advanced Methods in Applied Statistics
Course content
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.
MSc Programme in Environmental Science
MSc Programme in Physics
Knowledge:
- 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)
Skills:
- 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.
Competences:
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.
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++/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.
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.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Continuous assessmentWritten 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%) - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
-
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 assessment
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.
Single subject courses (day)
- Category
- Hours
- Lectures
- 36
- Preparation
- 90
- Practical exercises
- 32
- Project work
- 36
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NFYK15002U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 3
- Schedulegroup
-
A
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Physics, Chemistry and Nanoscience
Contracting department
- The Niels Bohr Institute
Contracting faculty
- Faculty of Science
Course Coordinator
- D. Jason Koskinen (8-6f73776f6d7269724472666d326f7932686f)
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Kursusinformation for indskrevne studerende