Specific Competition
CE8 - Know how to program, at least, in a relevant language for scientific calculation in Astrophysics
CE11 - Know how to use current astrophysical instrumentation (both in terrestrial and space observatories) especially that which uses the most innovative technology and know the fundamentals of the technology used
General Competencies
CG1 - Know the advanced mathematical and numerical techniques that allow the application of Physics and Astrophysics to the solution of complex problems using simple models
CG4 - Evaluate the orders of magnitude and develop a clear perception of physically different situations that show analogies allowing the use, to new problems, of synergies and known solutions
Basic skills
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
CB7 - That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader contexts
CB8 - That students are able to integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments
CB10 - That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous
6. Subject contents
Theoretical and practical contents of the subject
The theoretical and practical contents of the subject are divided into the following topics:
Topic 1. Introduction to programming with Python
1.1 Introduction to programming languages
1.2 Installation and startup
1.3 Data types
1.4 Operators
1.5 Modules
1.6 Operations and data structure
1.7 Control flow statements
1.8 Reading and writing files
1.9 User-defined functions
1.10 Running codes with exceptions
1.11 Passing arguments on the command line
1.12 Classes
1.13 Graphs
Topic 2. Statistical analysis of data
2.1 Introduction
2.2 Libraries
2.3 Material
2.4 Previous concepts: measurement; precision and accuracy; random and systematic errors; observable; bias; estimator; noise; data model
2.5 Characterization of measurements: mean value, median and mode; deviations; variance; significance; covariance matrix
2.6 Probability density distribution functions: continuous and discrete distributions; representation of distributions; obliquity/skewness and kurtosis; exercises
2.7 Probability functions: sampling of probability functions
Topic 3. Linear and nonlinear adjustments
3.1 Method of least squares
3.2 Nonlinear functions
Topic 4. Bayesian statistics
4.1 Information and entropy
4.2 Distance between probability functions
4.3 Bayesian Inference: axioms of probability theory; Bayes theorem; model comparison: evidence; steps in Bayesian inference; sampling of a lognormal-Poisson function
Topic 5. Fourier analysis
5.1 Introduction
5.2 Fourier theorem
5.3 Properties: convolution; Fourier transform and differential operators; famous transformations; filters; discrete Fourier transform; algorithms; bookstores
