Until recently, researchers working with data analysis were struggling to obtain data for their experiments. Recent advances in the technology of data processing, data storage and data transmission, associated with advanced and intelligent computer software, reducing costs and increasing capacity, have changed this scenario. It is the time of the Internet of Things, where the aim is to have everything or almost everything connected. Data previously produced on paper are now on‐line. Each day, a larger quantity of data is generated and consumed. Whenever you place a comment in your social network, upload a photograph, some music or a video, navigate through the Internet, or add a comment to an e‐commerce web site, you are contributing to the data increase. Additionally, machines, financial transactions and sensors such as security cameras, are increasingly gathering data from very diverse and widespread sources.
In 2012, it was estimated that, each year, the amount of data available in the world doubles 
. Another estimate, from 2014, predicted that by 2020 all information will be digitized, eliminated or reinvented in 80% of processes and products of the previous decade 
. In a third report, from 2015, it was predicted that mobile data traffic will be almost 10 times larger in 2020 
. The result of all these rapid increases of data is named by some the “data explosion”.
Despite the impression that this can give – that we are drowning in data – there are several benefits from having access to all these data. These data provide a rich source of information that can be transformed into new, useful, valid and human‐understandable knowledge. Thus, there is a growing interest in exploring these data to extract this knowledge, using it to support decision making in a wide variety of fields: agriculture, commerce, education, environment, finance, government, industry, medicine, transport and social care. Several companies around the world are realizing the gold mine they have and the potential of these data to support their work, reduce waste and dangerous and tedious work activities, and increase the value of their products and their profits.
The analysis of these data to extract such knowledge is the subject of a vibrant area known as data analytics, or simply “analytics”. You can find several definitions of analytics in the literature. The definition adopted here is:
- The science that analyze crude data to extract useful knowledge (patterns) from them.
This process can also include data collection, organization, pre‐processing, transformation, modeling and interpretation.
Analytics as a knowledge area involves input from many different areas. The idea of generalizing knowledge from a data sample comes from a branch of statistics known as inductive learning, an area of research with a long history. With the advances of personal computers, the use of computational resources to solve problems of inductive learning become more and more popular. Computational capacity has been used to develop new methods. At the same time, new problems have appeared requiring a good knowledge of computer sciences. For instance, the ability to perform a given task with more computational efficiency has become a subject of study for people working in computational statistics.
In parallel, several researchers have dreamed of being able to reproduce human behavior using computers. These were people from the area of artificial intelligence. They also used statistics for their research but the idea of reproducing human and biological behavior in computers was an important source of motivation. For instance, reproducing how the human brain works with artificial neural networks has been studied since the 1940s; reproducing how ants work with ant colony optimization algorithm since the 1990s. The term machine learning (ML) appeared in this context as the “field of study that gives computers the ability to learn without being explicitly programmed,” according to Arthur Samuel in 1959 
In the 1990s, a new term appeared with a different slight meaning: data mining (DM). The 1990s was the decade of the appearance of business intelligence tools as consequence of the data facilities having larger and cheaper capacity. Companies start to collect more and more data, aiming to either solve or improve business operations, for example by detecting frauds with credit cards, by advising the public of road network constraints in cities, or by improving relations with clients using more efficient techniques of relational marketing. The question was of being able to mine the data in order to extract the knowledge necessary for a given task. This is the goal of data mining.