These days, terms like data science, machine learning, and artificial intelligence are sometimes referred to as equivalents, although this is incorrect.
Below you can find what each one represents:
- Data science
Simply put, data science refers to the process of extracting useful insights from data. This interdisciplinary approach combines various fields of computer science, scientific processes and methods, and statistics to extract data in automated ways.
In order to gather big data, which is closely related to the field, data science uses a wide range of techniques, tools, and algorithms collected from the fields. Data science education promotes these techniques.
- Machine learning
In machine learning (ML), statistical methods are used to empower machines to learn without being explicitly programmed.
The field focuses on learning algorithms from given data, gathering information, and making predictions about unanalyzed data based on the information gathered. In general, machine learning is based on three basic learning algorithm models:
- supervised machine learning algorithms
- unsupervised machine learning algorithms
- reinforcement machine learning algorithms
In the first model, there is a dataset with inputs and outputs. In the second, the machine learns from a dataset that comes only with input variables. In the reinforcement learning model, algorithms are used to select an action.
- Artificial Intelligence
Although it is a broad term, at its core, AI refers to the process of building machines that allow for the simulation of the functioning of the human brain.
In the modern technological landscape, AI is divided into two main areas.
The first is general AI, which is based on the idea that a system can handle tasks such as speaking and translating, recognizing sounds and objects, conducting business or social transactions, etc. The other AI refers to concepts such as driverless cars.
How are all these fields connected to each other?
The interdisciplinary field of data science uses core skills from a wide range of fields, such as machine learning, statistics, visualization, etc. It allows us to identify meaning and relevant information from huge volumes of data to make informed decisions in technology, science, business, etc.
For a simpler view of the relationship between these technologies, AI is implemented based on machine learning. And machine learning is a part of data science that draws features from algorithms and statistics to process data coming from multiple sources. So, you can say that data science merges together a bunch of algorithms learned from machine learning to develop a solution, and in the process, borrows a lot of ideas from domain expertise, statistics, and mathematics.
