A Mortal’s Guide to Data Science
This is the most simple and comprehensive “Introduction to Data Science” article you’ll ever see.
I have a strong hunch that you have probably heard the term “Data Science” by now, as it is pretty much everywhere. It’s almost like everyone is trying to break into this field or is already in it.
Data science is one of the hottest fields in the world right now. It is a field that is growing quickly and changing the world we live in into a totally new arena. Data science has broken into a variety of different industries. It is already being used for a lot of different things, like making cars that drive themselves and helping to diagnose and treat diseases. So, it would be weird if you didn’t at least feel the urge to take a sneak peek at this famous field of “Data Science.”
So, What exactly is this Data Science?
Well, it is impossible to say data science is exactly this; data science is not that. There are many definitions of data science out there. This is the major reason why people can’t seem to get a good idea of data science, especially for those who are trying to get into the field.
It is important to hear the most famous definitions of data science to get an intuitive idea of the field.
“The first rule of data science is: don’t ask how to define data science.” — Josh Bloom
Well, this person made it a rule not to ask for it from him, which is pretty much understandable.
“Defining data science is like defining the internet — ask 10 people and you get 10 different answers.”
— Micaela S. Parker, Arlyn E. Burgess, and Philip E. Bourne
As I said, it can’t be defined to the point. Honestly, it feels like you can literally connect everything and anything to data science during that process. Creating boundaries around the field to define data science takes away its meaning.
“The application of data-centric computational and inferential thinking to understand the world and solve problems.”
— Joseph Gonzalez, an assistant professor at U.C. Berkeley
Now, this is not the most mortal-friendly and simplest definition with all the geeky words. But it’s the shortest and simplest definition, at least to me when I was trying to understand it myself. Later, I kind of developed a definition for myself using my own words as others’ words felt like Greek to me.
Data science is a combination of various disciplines that use statistics, algorithms, and many other scientific methods for gathering, processing, and analyzing data to look at data and figure out exactly what it meant, what it means and what it will mean in the future according to our purpose.
—definition of Charmie Ranodya at the time of writing this article.(on 09/16/2022 at 10.57 pm)
Do you know what is fascinating? This is like the 3rd definition of “Data Science” that I made for myself. It changes as I explore and learn more. I’m pretty sure the above-mentioned definition will also change in the near future.
But I’m certain that my current definition is more simple, more lightweight, and easier to comprehend than many other definitions you’ve come across and will come across.
As you now have a rough idea of what data science is, you might be wondering,
Okay, why do we need this?
Data is the most valuable asset for any business. We can turn data into clear business advantages if we have the right tools, technologies, and algorithms by using Data Science.
For example,
- Data science can help you detect fraud using advanced machine learning algorithms. Don’t give much thought to terms like “machine learning” for now. Just keep in mind that data science can detect fraud, which we as humans won’t see directly.
- It can aid you to minimize or completely prevent any significant monetary losses. How? For example, you can decide if it is safe for you to lend money to someone by using Data Science.
- It enables us to imbue machines with intelligence, making life easier for us humans.
- It enables us to make better and faster decisions by using data.
Interesting, right?
Now that you have an idea of data science and its importance, I believe that the next burning question is “How do all these work?”
Actually, this “working” is a process that consists of a number of steps, and each one is important. To get to the desired end result, one should always take the right steps, and each step is important and has its own value. And this process is known as Data Science Life Cycle.
Now slow down and process the above-mentioned information before you fasten your seat belts and get ready to find out about those steps since it is a concept that deserves a whole another article.
And I’ll publish that article as soon as I can.
I would be delighted to hear your thoughts on this article as this is my first ever technical article. Thank you so much for reading this and I hope it was worth your time.
