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Astrostatistics Summer School Crete 2019 – last day

During the last four days I was busy with the actual materialization of the Astrostatistics Summer School Crete 2019, which took place at the University of Crete, in Heraklion (18-21 June 2019). My duties were mainly those of the Teaching Assistant and I contributed with a short presentation of the Random Forests method for classification. Unfortunately I didn’t have the time to post more on this school, so I ended up doing something only today, at the very last day!

In summary this is/was a school for graduate and early-stage post-docs to get a grasp of the modern field of Astrostatistics, which practically means the application of statistics in Astronomy which incorporates also machine-learning techniques. Topics include: Intro to Python and Jupyter notebook, linear regression, classical statistical distribution tests and hypothesis testing, Bayesian statistics, Markov-Chain Monte Carlo, machine learning classification/regression/clustering, time series analysis.

The school is split in “teaching”/explanatory parts (through Jupyter notebooks though where you could also interact and run the examples) and practical workshops, where important hands-on experience with all the tools presented was provided (and hopefully gained!). However, I think the importance lies in the fact that all the material is publicly available through a github repository: https://github.com/astrostatistics-in-crete (including the notebooks with the introduction notes, the exercises in the workshops, and the data). So this is a valuable source both for students of the school, as well as others interested to try and experiment with these tools.

Workshop on Python and IRAF/PyRAF

Between 12 to 19 of September 2016, we organized the Ondřejov Summer School 2016, which was held at the Stellar Stellar Department of the Astronomical Institute (Czech Academy of Sciences), in Ondřejov. The main aim of the school was to provide students hands-on experience with real observational data, i.e. to understand observations, reduce them, and discuss their results. This project-oriented activity was supplemented with lectures on various techniques and one of them was a workshop on Python and IRAF/PyRAF.

As a workshop the main focus was given to show examples that the students could repeat at the own computers. Its structure was based on a very small introductory talk and then examples of how to use Python and IRAF in real problems. With this post I make public all of this material both as a reference and as a (hopeful) help to others.

Material:

  • Introductory talk [ .pdf / .odp ]
  • Talk images [ .tar.gz ]
  • Videos – (I need to work on this !)
  • IRAF files (will be added later)

    All of this work is licensed under CC-BY-SA – which means that you can work on it and expand!
    If you like it and want to refer to this work, please cite it as:
    G. Maravelias 2016, “Workshop on Python and IRAF/PyRAF”, Ondřejov Summer School 2016, Ondřejov, Czechia
    online at: http://maravelias.info/2016/11/workshop-on-python-and-irafpyraf