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

Activity no.: 5888-19-11-31 
Enrollment deadline: 18/11/2019
Date and time18.11.2019, at: 00:00 - 26.01.2020, at: 00:00
Regular seats30
ECTS credits7.50
Contact personJulie Meier    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 assignment
Exam detailsThe written assignment will be 28 hours. The final grade is normally given based on the continuous evaluation, as well as on the take-home exam, with the following weight: 25% from projects, 15% from problem sets, and 60% from the 28-hour 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.
Grading scalePassed / Not passed

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
With this course, the student should 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 ChiSquare probability.

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.

Target group

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.

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).

PhD students should enroll via this page.

MSc students: please go to https://kurser.ku.dk/ to sign up for the MSc course.

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