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

Activity no.: 5888-22-11-31
Enrollment deadline: 21/11/2022
Date and time21.11.2022, at: 00:00 - 29.01.2023, at: 00:00
Regular seats30
ECTS credits7.50
Contact personAiga Voite    E-mail address: aiga.voite@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 assignment
Exam detailsWritten assignment, 36 hours. The final grade is 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.
Exam aidsAll aids allowed
Grading scalePassed / Not passed
Criteria for exam assessmentSee learning outcome
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

Sign-up: 

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

MSc students sign up via kurser.ku.dk.

Please contact Aiga Voite 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.
  • Understanding how to use probability distribution functions.
  • Be able to calculate, propagate and interpret uncertainties.
  • Be capable of fitting data sets and obtain parameter values with uncertainties.
  • Know the use of simulation in planning 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 from essentially 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 literature: Statistical Data Analysis, Glen Cowan. Data Reduction and Error Analysis, Philip R. Bevington. Statistical Methods in Experimental Physics.


Teaching and learning methods
Lectures, exercises by computers, and discussion/projects.

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

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).

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