Introduction to KDD and Data Science

Abstract

Nowadays, the availability of large volumes of data and the widespread use of tools for the proper extraction of knowledge information has become very frequent, especially in large corporations. This fact has transformed the data analysis by orienting it towards certain specialized techniques included under the umbrella of Data Science. In summary, Data Science can be considered as a discipline for discovering new and significant relationships, patterns and trends in the examination of large amounts of data. Therefore, Data Science techniques pursue the automatic discovery of the knowledge contained in the information stored in large databases. These techniques aim to uncover patterns, profiles and trends through the analysis of data using reconnaissance technologies, such as clustering, classification, predictive analysis, association mining, among others. For this reason, we are witnessing the development of multiple software solutions for the treatment of data and integrating lots of Data Science algorithms. In order to better understand the nature of Data Science, this chapter is organized as follows. Sections 1.2 and 1.3 defines the Data Science terms and its workflow. Then, in Sect. 1.4 the standard problems in Data Science are introduced. Section 1.5 describes some standard data mining algorithms. Finally, in Sect. 1.6 some of the non-standard problems in Data Science are mentioned.