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Applied Statistics: From Data to Results
Provider: Faculty of Science

Activity no.: 5888-21-11-31
Enrollment deadline: 01/11/2021
Date and timeNovember 2021 - January 2022
Regular seats30
ECTS credits7.50
Contact personJulie Meier Hansen    E-mail address: juliemh@nbi.ku.dk
Enrolment Handling/Course OrganiserTroels Christian Petersen    E-mail address: petersen@nbi.ku.dk
Written languageEnglish
Teaching languageEnglish
Study boardStudy Board of Physics, Chemistry and Nanoscience
Semester/BlockBlock 2
Scheme groupB
Exam formContinuous assessment
Exam formWritten examination
Exam detailsWritten assignment, 36 hours The final grade is normally given based on the continuous evaluation as well as on the take-home exam with the following weight; 20% from a project, 20% from a problem set, and 60% from a 36 hours take-home exam. It is possible to some extent to arrange a different weight in individual cases in agreement between the student and course responsible, if this can be justified.
Exam aidsAll aids allowed
Grading scale7 point grading scale
Exam re-examinationThe exam form is identical to the regular exam; the project and/or problem set that were approved during the course can be re-used. The remaining project and/or problem set should be approved 2 weeks before the re-exam.
Course workload
Course workload categoryHours
Lectures56.00
Preparation98.00
Theory exercises28.00
Exam24.00

Sum206.00


Content

Remarks:

It is expected that the student brings a laptop. There will be an introduction the week before the course begins. You will be informed about time and place later (on the course webpage).

PhD students should apply via the credit student application » at this link. The Course Code to enter is NFYK13011U

Please contact Julie Meier Hansen (juliemh@nbi.ku.dk) if you have any questions or problems re. sign-up.

 


Aim and content
The course will give the student an introduction to and a basic knowledge on statistics. The focus will be on application and thus proofs are omitted, while examples and use of computers take their place.

The course will cover the following subjects:
• Introduction to statistics
• Distributions - Probability Density Functions
• Error propagation
• Correlations
• Monte Carlo - using simulation
• Statistical tests
• Parameter estimation - philosophy and methods of fitting data
• Chi-Square and Maximum Likelihood fits
• Simulation and planning of an experiment
• Multidimensional data and Fisher Discriminant
• Introduction to Machine Learning
• The power and limit of statistics.

Formel requirements
Programming is an essential tool and is therefore necessary for the course (we will use Python with interface to CERN’s ROOT software, both free and working on all platforms). The student should be familiar with different types of variables, loops, if-sentences, functions, and the general line of thinking in programming. Elementary mathematics (calculus, linear algebra, and combinatorics) is also needed.

Learning outcome
Skills
The student should in the course obtain the following skills:
• Determining mean, width, uncertainty on mean and correlations
• Understading how to use probability distribution functions
• Be able to calculate, propagate and interprete uncertainties
• Be capable of fitting data sets and obtain parameter values with uncertainties
• Know the use of simulation in planing experiments and data analysis
• Select and apply appropriate statistical tests.

Knowledge
The student will obtain knowledge about statistical concepts and procedures, more specifically:
• Binomial, Poisson and Gaussian distributions and origins
• Error propagation formula – use and applicability
• ChiSquare as a measure of Goodness-of-fit
• Calculation and interpretation of p-values
• Determination and treatment of potential outliers in data
• The applicability of Machine Learning.

Competences
This course will provide the students with an understanding of statistical methods and knowledge of data analysis, which enables them to analyse data in ALL fields of science. The students should be capable of handling uncertainties, fitting data, applying hypothesis tests and extracting conclusions from data, and thus produce statistically sound scientific work.

Literature

See Absalon for final course material. The following is an example of expected course literature.

Primary literature: Statistics - A Guide to the Use of Statistical Methods in the Physical Sciences, Roger Barlow.
Additional literatur: Statistical Data Analysis, Glen Cowan. Data Reduction and Error Analysis, Philip R. Bevington. Statistical Methods in Experimental Physics.


Lecturers
Troels Christian Petersen (petersen@nbi.ku.dk)

Remarks


Sign-up:

PhD students should apply as credit students » at this link. The Course Code to enter is NFYK13011U. 

If you apply for this course within a month of the start date, please let us know that your application is on the way!

Please contact Julie Meier Hansen if you have any questions.

 


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