Two possibilities exist: either we are alone in the Universe or we are not. Both are equally terrifying.
— Arthur C. Clarke
As human beings nothing seems to us more important than discovering the universe. Not just what is around us on the Earth or in the solar system, but also, and specially, what exist far beyond our galaxy, cluster and supercluster. Is someone there? Are we the only intelligent cells who can help the Universe to discover itself? How big is it and which components exist in it? How was it in the past and what will happen to it in the future?
There are tens of such questions that we may ask ourselves everyday. In the history of mankind, there were a lot of philosophers who tried to answer these questions. Today, we know that the only reliable answers come from the science. By the term science, we mean only one kind of knowledge which is testable by empirical data.
We have the great chance of living at a time that a huge amount of observational data are publicly accessible on the internet. There are several astronomical data centers with billions of entries which can be queried by anyone with a personal computer. The scientific material is also available publicly. Everything is provided for us to engage in this discovery. What remains is the personal effort and curiosity.
In the age of big data, all the branches of science are unimaginable without data science. This is specially the case of astronomy. You may have heard the term data driven astronomy or astroinformatics. I prefer astro-data-science because there’s nothing in data by itself. It is the human knowledge and intelligence that converts meaningless data to useful information. This is why I’ve chosen the name of this website.
To be an astro-data-scientist, first of all, you have to know astronomy. Then, you should know at least one programming language. In this website, most of the time we will use python. Its simplicity, popularity and the extensive libraries created by scientific teams make python our first choice. It helps you focus on the scientific aspects rather than be confused with programming problems. Although you don’t need to become a professional programmer to be astro-data-scientist, you must know data science.
Data science covers a lot of topics, such as data engineering, data analysis, machine learning, artificial intelligence and so on. But it is essentially based on mathematics, probability and statistics. This basis lets you create different models, from simple regression models to sophisticated neural networks. We will cover all of these in this website in different tasks, for example, data reduction for classification of galaxies, image processing to search for patterns in scanned images of the sky, neural networks for predicting characteristics of objects by their spectra and etc.
With academic background in different fields, from engineering to social sciences and urban planning, I am an enthusiast of astronomy and data science. Any feedback would be appreciated and all types of cooperation on the subject of this website are welcome.