Applied Statistics: From Data to Results
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
The course will cover the following subjects:
- Introduction to statistics.
- Distributions - Probability Density Functions.
- Error propagation.
- 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.
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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.
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.
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.
Lectures, exercises by computers, and discussion/projects.
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.
Programming is an essential tool and is therefore necessary for
the course (we will use Python). 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
Academic qualifications equivalent to a BSc degree is recommended.
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 and by Email).
- 7,5 ECTS
- Type of assessment
Continuous assessmentWritten assignment, 36 hours
- Type of assessment details
- 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.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
several internal examiners
Criteria for exam assessment
Single subject courses (day)
- Theory exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
- No restriction
The number of seats may be reduced in the late registration period
- Study Board of Physics, Chemistry and Nanoscience
- The Niels Bohr Institute
- Faculty of Science
- Troels Christian Petersen (8-72677667747567704270646b306d7730666d)
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Courseinformation of students