Data Science

What is Data Science?

The terminology Data Science has been used over a long period of time, however, it is not until the 70s when the term begins to be used to refer to data processing methods. It was in 2001, when data science was separated from the Big Data, and proclaimed as an independent discipline.

Main differences between Data Science and Big Data

Big Data is characterized by its “7V“: Volume, Variety, Velocity, Veracity, Value, Visualization, Variability. It brings together information of different types and sources while Data Science has the necessary techniques to analyze these volumes of data.

Data Intelligence provides incredible performance potentials. However, it is Data Science that provides the theoretical and experimental side, as well as providing a deductive and inductive process.

Data science uses intelligent models that learn from themselves, such as machine learning, along with statistical methods to train computers. In contrast, Big Data is responsible for the extraction of useful information found in large data sources

It can be said that Data Science would not exist if it were not for Big Data, since it develops within its scope. However, Big Data would not have (or would not obtain) its current value if it were not for the analyses and methods used by Data Science.

The key concepts of Data Science

Machine learning is possible thanks to certain algorithms that identify and learn from patterns such as:

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