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Applied Statistics: From Data to Results - PhD course
Provider: Faculty of Science

Activity no.: 5888-20-11-31
Enrollment deadline: 31/08/2020
Date and timeNovember 2020 - January 2021
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 formAndet/Other
Exam details Written 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 scalePassed / Not passed
Course workload
Course workload categoryHours
Lectures56.00
Preparation98.00
Theory exercises28.00
Exam24.00

Sum206.00


Aim and content
The course will give the student an introduction to and a basic knowledge of statistics and data analysis. The focus will be on the application of statistics and thus proofs are omitted, while examples and use of computers take their place. For this reason, programming plays a central role and is an essential requirement (see below).

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.


Enrollment information

The course is offered both for M.Sc. and PhD students. If you are enrolled as a PhD student in Denmark, please sign up for the M.Sc. course at the following link
https://kurser.ku.dk/course/nfyk13011u/  as a credit student.

MSc students: sign up for the course via https://kurser.ku.dk/course/nfyk13011u/

 

Important information for students outside of Denmark:
To apply for participation in this course, it is required that you send an email to the course organizer with your information and motivation for joining the course. Do not use the online application. Thank you.

 

 


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.
• Know the use of simulation in planing experiments and data analysis.

Knowledge
The student will obtain knowledge about statistical concepts and procedures, more specifically:
• Binomial, Poisson and Gaussian distributions and origins.
• Error propagation formula and how to apply it.
• 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 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)

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.
•The power and limit of statistics. The frontier.

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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