These days, terms like data science, machine learning and artificial intelligence they are sometimes referred to as equivalents, though they are wrong.
Below you can find what each one stands for:
- Data science
Simply put, data science refers to the process of extracting useful data from the data. This interdisciplinary approach combines different areas of computer science, the scientific processes and methods, and statistics to export data in automated ways.
In order to collect big data, which are closely related to the field, data science uses a wide range of techniques, tools and algorithms collected from the fields. Data science training promotes these techniques.
- Machine learning
In machine learning (ML), statistical methods are used to empower machines to learn without explicit programming.
The field focuses on learning the algorithms from the data provided, gathering information, and predicting data that has not been analyzed, based on the information gathered. In general, mechanical learning is based on three basic models of learning algorithms:
- 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 one dataset which only comes with input variables. The reinforcement learning model uses algorithms to select an action.
- Artificial Intelligent
Although it is a broad term, at its core, AI refers to the process of building machines that allow the simulation of the functioning of the human brain.
In the modern technology landscape, AI is divided into two main areas.
The first is general AI, which is based on the idea that one system can handle tasks such as speaking and translating, recognizing sounds and objects, doing business or social transactions, etc. The other AI refers to concepts such as driverless cars.
How do all these fields relate to each other?
The interdisciplinary field of data science uses key skills in a wide range of fields, such as machine learning, statistics, visualization etc. It allows us to identify meaning and 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 based on machine learning. And machine learning is a part of data science that draws features from algorithms and statistics to process data from multiple sources. So you can say that data science merges together a bunch of algorithms acquired by machine learning to develop a solution, and during the process, borrow many ideas from their experience. domain, statistics and mathematics.