There are a limited number of places available for each tutorial. The

Tutorial 1

Title: Ridiculously Advanced Python

Author: Francesco Pierfedeerici (fpierfed@iram.es)

Location: Room: 10 & 11a

If you have been using Python for some time already and want to reach new heights in your language mastery, this training session is for you! Python has a number of features which are extremely powerful but, for some reason are not particularly well known in the community. This makes progressing in our Python knowledge quite hard after we reach an intermediate level. Fear not: this session has you covered! We will look at some advanced features of the Python language class decorators, type annotations, data classes and meta-classes. If time allows we will even delve into the abstract syntax tree (AST) itself. We will use Python 3.7 and strongly recommend that attendees install a reasonably recent version of Python 3 to make the most out of the training. The tutorial session will be in the form of live coding with participants encouraged to merrily type along. Warning: some of the topics presented will almost certainly assure an early end to an otherwise successful career in software engineering.

Tutorial 2

Title: Machine learning

Author: Kai Polsterer (Kai.Polsterer@h-its.org)

Location: Room 11b & 12

I really would like to do a tutorial or 2 directly following each other on the Sunday at the upcoming ADASS. Perhaps I could get another person assisting me so I survive a 4 hour show. Participating at ADASS is already fun, but helping to shape it is even better. Considering the last meeting and the feedback I got, I could think of something like: A hands on introduction to neural networks. From simple neurons to deep neural architectures. (I would put it in a jupyter notebook and ask people to run thinks while we talk. Depending on the assistance I get, I might even do it more interactively as the last meetings BoF. Here I would like to get some feedback what you would like to see at ADASS) or: Advanced guide to machine learning in astronomy. Form images to time-series. (This would be a direct follow up for the last BoF the beginners guide and focus on more advanced topics. Also as jupyter notebook) you’ve got the choice. Just let me know, what you like to see at the next ADASS. Most things in the direction of machine learning I should be able to be prepare.

Tutorial 3

Title: Interactively exploring and visualizing data on the sky with Jupyter and pywwt

Author: Peter Williams (pwilliams@cfa.harvard.edu)

Location: Room 10 & 11a

Astronomers routinely work with images of the sky or tables of celestial objects. In this tutorial, participants will learn how to explore such data sets interactively, and in their astronomical context, inside Jupyter notebooks. The key enabling technology is pywwt, a module that allows researchers to embed the WorldWide Telescope visualization engine in Python applications and Jupyter notebooks, providing a sophisticated “ds9-like” experience inside the notebook that can be controlled through code as well as manually. Hands-on activities will stitch this Python module together with other elements of the Python ecosystem for working with astronomical data such as astroquery. Participants will learn how to explore their sky-based data sets using a modern suite of software tools.

Tutorial 4

Title: DesignThinking

Author: PeFelix Stoehr (fstoehr@eso.org)

Location: Room 13

The tutorial aims at exposing the ADASS participants to modern methods of user-centric design, in particular to the DesignThinking method. These methods are very widely used throughout the industry but it is probably fair to say that they have not yet really arrived in the astronomcal science- and software community. With the very user-oriented work that the ADASS community is carying out, DesignThinking could bring large benefits.