Hello everyone. We are pleased to introduce the curriculum of data science for astrophysics for the master's degree in physics at the University of Insubria in Como. For the next academic year there are two big news: first is that the characterizing courses have been completely renewed in order to offer a preparation focused on the data science. This will allow the student to start an absolutely modern and up-to-date education in astrophysics acquiring advanced knowledge and skills required today in a multitude of different areas. The other big news is that all the activities will be performed in full cooperation with scientists and researchers of INAF the Italian Institute of astrophysics. With INAF we already have a partnership at the level of doctorate since many years. Lectures and therefore the possibilities of research theses who benefit from the interaction with the local branch of INAF namely the astronomical observatory of Brera where more than 50 scientists are employed in the most various fields of astrophysics from theory to observations to technological developments of space missions. The idea behind the new curriculum is twofold: first we want to offer really innovative and beyond state-of-the-art courses and second we aim at exploiting in a more systemic way the immense knowhow present in INAF DNA. These are the four characterizing courses that we propose within the curriculum of data science for astrophysics. They are completely new courses in the content, all implemented with practical exercises and analysis of particularly relevant case studies. Before analyzing the content of individual teachings in a detailed way let's see what could be the path for a master’s degree in data science for astrophysics. In addition to the characterizing courses a full immersion course in scripting and programming is mandatory: Python will be the language to address many of the problems you’ll meet during the route. General relativity is a must that cannot absolutely miss in the baggage of knowledge of a physicist in particular of an astrophysicist as well as knowledge about the physical principles of radiation detectors. The other four suggested courses supply in a complementary way knowledge that is certainly useful to a theoretical numerical approach of the data science for astrophysics with an eye also on observational technologies. Let's take now a closer look at the contents of the four characterizing courses. Let's start with the basic teaching elements of astrophysics and cosmology: the course will begin facing the problem of the formation, evolution and final destiny of the Stars;,starting from a cloud of molecular hydrogen helium and metals to end up with white dwarfs, neutron stars and black holes. The physics of this compact objects will then be discussed with particular reference to the observational techniques required for their detection. Before leaving the Milky Way we will face the fascinating and new world of extrasolar planetary systems. Then we will study the formation and evolution of galaxies from the large spirals such as the Milky Way and Andromeda to remote irregular galaxies which were formed about thirteen billion years ago at the beginning of the history of the cosmos. Among the galaxies a particular emphasis will be given to the so called active galactic nuclei where supermassive black holes weighting millions to billions solar masses are responsible for phenomena among the most energetic and spectacular of the entire universe. Finally we will see what modern cosmological theories tell us about the very nature of our own universe. We will try to answer questions such as how was the universe born how has it evolved what is its final destiny. Let's move now to the teaching on artificial intelligence for Astrophysical problems. We will learn to understand and use machine learning techniques with a focus on the solution of many different Astrophysical problems. The importance of the classification algorithms will be analyzed in detail with an in-depth study of the different existing algorithms and of their use. Students will learn to master image recognition algorithms: from face recognition to galaxy recognition the step is really short. All the subjects will be supported by examples and case studies and innovative concepts such as numeral networks and deep learning will be applied. Then one of the most advanced and fascinating fields of data science will be covered: that is unsupervised machine learning. And finally we will see how astrophysics and the big data world are now in terms related. Computational astrophysics will be dedicated to learning the advanced concepts playing behind Astrophysical and cosmological state of art mastered numerical simulations. The teaching will start with an overview of the numerical techniques necessary for the solutions of Astrophysical problems. We will apply what we learned first in dynamical systems such as n-body systems star clusters and planetary systems. Then to the solution of the equations of hydrodynamics analyzing the types of existing numerical codes SPH EMR moving mesh and finally we will learn the numerical techniques necessary to solve the problem of radiative transfer in Astrophysical systems. Then with those techniques at hand we could simulate the formation of the large-scale structure of the universe through multi scale HPC. Finally we will understand how massive numerical simulations are intimately linked to big data. In the fourth course we are proposing we will begin by developing the knowledge of variable phenomena in astrophysics seen as a fundamental source of scientific information and we'll begin with an overview of the main variable phenomena in the cosmos. In this photo you see Jocelyn Bell at the time of her discovery of the pulsars in 1967 when she was just 24. Then we will learn the fundamental tools for the analysis of variable signals that is Fourier analysis with applications to stellar variability and to EXO planet transits and naturally to pulsars. We will analyze in detail the so called autoregressive phenomenon for example the variability of active galactic nuclei. Then we'll move on to nonparametric analysis a fundamental tool that finds application for example in the analysis of gravitational wave signals from binary black holes or in the analysis of the anisotropies of the Cosmic Microwave Background the Echo of the Big Bang. We will see then how Big Data machine learning and intelligent systems are now essential concepts in the analysis of variable Astrophysical signals. And finally a particularly instructive case study that is the analysis of the SETI signal search for extraterrestrial intelligence. Here you see a young Jodie Foster in the film Contact who is listening to radio signals coming from alien civilizations. That's all for now. We hope you enjoyed the presentation of data science for astrophysics. It is important to stress what I said at the beginning that what you will learn during your master years in Insubria, concepts such as machine learning neural network Big Data high performance computing and many others, represents a know-how which will be instrumental in shaping your future professional career certainly not limited to astrophysics. Astrophysics however represents one of the frontiers of human knowledge where such concepts find perhaps their fullest application. Thanks for your kind attention.