Data Science is a data science that combines different areas of knowledge: computer science, mathematics, and systems analysis. This includes methods for processing big data (Big Data), data mining (Data Mining), statistical methods, methods of artificial intelligence, including machine learning (Machine Learning). DS includes methods for the design and development of databases and application software.
A little history: how data science appeared and why is big data here
Unlike the term “big data,” which has become popular since the 2010s, data science began much earlier, in the second half of the 20th century. The first mention of this concept dates back to 1974, when the book by Peter Naur was published. In this publication, Data Science is defined as the discipline that studies the life cycle of digital data, from its inception to transformation and uses in other fields of knowledge. Nevertheless, this term received widespread use only in the 1990s and became generally recognized only in the early 2000s. In particular, in 2002, the interdisciplinary Committee on Data for Science and Technology began publishing the CODATA Data Science Journal, and in January 2003, the first issue of The Journal of Data Science at Columbia University was published.
The next wave of interest in DS arose with the popularization of the concept of Big Data, since 2010, when the computing power of even household computers began to allow working with large amounts of data. From about the same time, numerous professional conferences began to be held, and universities around the world have included this discipline in their training courses, developing appropriate educational programs.
Today Data Science is actively used in a wide range of applied fields of activity: from astronomy to medicine, including commercial cases:
- marketing;
- retail;
- management;
- financial analysis;
- predictive analytics of emergencies, etc.
Big data analytics
Big data analytics is the method of examining big and complicated data sources to identify courses, client code models, and market preferences to help you make more reliable marketing choices. The complexity of large data analytics needs new analytical tools such as forbidding analytics, machine learning, running analytics, and methods such as database and group analysis.
The Impact of Big Data Analytics
At the core of analytics is changing data into insightful activities that add cost to an arrangement, but the growth of structured and disorganized data, also recognized as big data, has revolutionized the way analytics function.
While big data has expanded the possibilities available to companies, it has additionally created more challenges for collecting, saving, and obtaining data.
This revolutionary shift brings important extra accommodation requirements and challenges to analytic software. It additionally produces powerful opportunities for discovering and implementing new strategies to develop a competing service.
Understanding these opportunities requires two things: the technological capacity to obtain and collect big data, including new tools to transform data into shrewdness and ultimately value.
Benefits of a big data analytics solution
They are:
- A unified way of marketing
With a centralized data structure, companies are provided with a large, regular, and complete data window that improves choice-making and allows users to work with the most accurate and timely information.
- The fastest time for business
Big data analysis services improve decision-making productivity by allowing everyone in the company to predict circumstances and possibilities, suggest important and appropriate problems, and receive results that lead to a certain development.
