This article series consists of 4 main parts and this article is “What is data science?” is the last in the series.
Note: I usually will use some abbreviated words below:
- Data Science — DS
- Artificial Intelligence — AI
- Machine Learning — ML
- Big Data — BD
- Deep Learning — DL
Lets talk about DS, AI and ML?:
AI has never been among the things I have written so far.
So where is AI at work?
In response, we can consider using DS as a lever to explain the relationship between ML and AI. If you do a research online, you will find that people are a bit confused about the relationship between these three disciplines. While some include ML in DS, others tend to perceive it as a part of AI. We are not going to come out with a classification that will end all discussions. But let’s see what we understand in general.
Let’s start with an observation.
In the past, Physics was seen as a sub-branch of Philosophy. As time passed, physics advanced and is now seen as a science in its own right. Similarly, while Probability Theory was a field of research within Mathematics, it branched out, giving birth to what we call Statistics. Therefore, whether the fields or disciplines are evaluated within each other depends on the decision of the time. In conclusion, we can say that everything is subject to the stipulation of this principle.
According to its acceptance by everyone, DS is a discipline that aims to obtain useful information from data. From this point of view, its scope is very wide. Consequently, it can be said that it is too early to make a full definition. AI is a field that aims to enable computers to think like humans. Due to its nature, DS makes extensive use of ML algorithms. Similarly, AI is one of the disciplines that make extensive use of ML techniques today.
In particular, DL, one of the ML methodologies, has been the main development area that paved the way for AI in the last 10 years. But as far as you can see, ML is not a discipline in the entirety of AI, because ML includes descriptive algorithms as well as predictive algorithms. On the other hand, AI is one of the fields contributing to DS. A lot of techniques, from genetic algorithms to fuzzy logic, find application in DS. Therefore, it would not be wrong for us to think of these three fields as three different disciplines that have a lot in common.
That’s all I have to share about DS. It would make me happy if I could use it as an article that will give you a new, different perspective!
My next post will be about “Super Hero Data Scientist”. See you as soon as possible …
Do not forget!
“In life, the most real guide is science … MKA”
“and you can explain this with data… MA”
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