A beginning ...

 Let's begin from something familiar...

I am pretty sure that everyone in here has seen this picture at least once in either WhatsApp, Instagram or any other social media platforms. But you all will be quiet surprised to know that this complex James Webb Space Telescope picture used Python- ya you heard it right PYTHON.. Astronomy and data science are actually pretty similar to each other. I mean honestly, don't you think that telescopes like JWST and HST produce like millions and millions of images. Then how do we go from those all million pictures to this single picture above. Yes, you guessed it right Data Analysis. The variety of images that we received are organized and filed by the data scientists here. Then they are classified and then further archived for future purposes. 

Imagine if I only gave you this picture and nothing else What would you think about it? A good wallpaper or a screensaver right. Well without the metadata, this picture is absolutely nothing but a JPEG image. So what type of data are these ? Well it actually varies from just binary data for the flight recorders to ephemeris data which provides information on positions of celestial object and further more. All of these data gets stored for further purposes. What purposes you might ask ? I will cover that in another post. 

Now, You might have a thought that all they do is just save and archive the file well I can do that too but what's the challenging part here though ?. Well, It's not that simple. Lets take a certain example-You are staring at space for a pretty long time to get a particular impression of let's say an exoplanet or a distant star by analyzing wavelengths to get the statistical certainty. The more longer and deeper you go, you will have much more wider field and it will be very crowded because of light sources from distant objects. You also might have cosmic rays which is basically extra energy which is not from your object. So, you can't manually check millions of values to check the anomalies in the data set. That's where we develop a pipeline. A pipeline is a very important part of data science and machine learning. Basically, in a very brief way data science pipelines refers to the process of collecting raw data from multiple sources, analyze it and finally present it in a understandable format. These pipelines are made and tested using various machine leaning models and at the end, it just helps all the scientists to discover new planets, stars and further more. 

Universe is a very, very, verrryyyyyy broad term. At the end, we honestly are capable of discovering so much but the implementation of data science in recent days has made it honestly very fun even for general public. The data archived is always available in multiple websites and in general astronomers from all over the world access and discover multiple exoplanets, stars and other celestial beings. This concludes this post, I hope you all got a brief understanding of how data science works and I will be posting soon about other implementations and even the whole concept of Data-Driven Astronomy.

Stay tuned and THANK YOU!!