I would like to share this series of articles in 4 main sections more fluently. This article “What is Data Science?” is the first in the series.
Data Science(DS) is undoubtedly one of the most popular research and application areas of today. The number of people who want to learn DS, which should be regarded as an interdisciplinary field by its nature, is increasing day by day. As a Data Scientist and researcher, I decided to create a roadmap and share it with my followers in order to guide those who want to learn DS, in the light of the experiences I have gained through the DS trainings I have received and given.
Note: I usually will use some abbreviated words below:
- Data Science — DS
- Artificial Intelligence — AI
- Machine Learning — ML
- Big Data — BD
- Deep Learning — DL
Data in general; They are different types of knowledge acquired as a result of different actions such as observation, research and experience, often shaped in a certain way. For example, a written text, numbers, information and facts we store in our minds, or files in memory represent data.
So what is the Data Science?
Let’s talk about the concept of “Data Science”, which has become very widespread in the world in recent years with the ease of producing, storing and transmitting data.
What is it real and where did it come from?
We can forget the complex definitions we have read so far and say that it is a method of extracting useful information from data for DS. Friend suggestions in social media applications (Instagram, Twitter, etc.), customization of ads on Google according to the user, and recommendation systems are a few examples that we see the power of this method.
So how does the process in DS progress?
An information emerges by applying various data analytics methods to the data sources we have. For example, if we think for a company, the fact that the company converts this emerging information into monetary gain by using it in appropriate actions shows that DS has been realized.
We live in a world where data flows in large amounts and diversity, and everyone talks about data as a very important source of information.
So what kind of information do we aim to access from the data?
Let me give a simple answer:
- We want to get useful information about the past, main and future from the data. In other words, the time period of the information we want to access is as important as whether this information is useful or not.
- When we come across observations similar to our data but we have not encountered before, we want to be able to make sense of them within the framework of our data. In other words, we want to classify our new observations we do not know within the categories we know.
The answer above expresses the purposes in terms of DS.
So, how does DS do it as it takes us to these goals, so what does it benefit from?
The method, which is the result of the combination of the Mathematics department, which covers learning concepts such as basic Mathematics, Statistical modeling and ML, Computer Science including programming languages, Database applications and Cloud systems, and Business-sector knowledge, where personal skills such as creativity and innovation are important, is DS.
So, before we talk about the work we aim with DS, let’s start by further explaining the four important elements in the composition of DS.
My next post is “What do we aim to do with Data Science?” See you…
Do not forget!
“In life, the most real guide is science … MKA”
“and you can explain this with data… MA”
My other Articles:
- James Wainscoathttps://handong1587.github.io/data_science/2015/10/09/data-science-resources.html