In the book Introduction to the data mining concepts and methods, data mining methods are described. Data mining means discovering and extracting knowledge from large amounts of raw data. Since the only useful tool to use past events analysis in databases to predict the future will be data mining, consequently, we will need data mining to discover and extract information from the statistics considered by the planners and managers of each organization. On the other hand, the present age is the age of novel and diverse methods of producing, storing and collecting data and statistical information. Policymakers are faced with a huge amount of diverse statistical information that is produced tremendously in a fraction of the time, in important and key organizations of the country. Data mining is a way to find the best knowledge and information from this multitude of microdata. Access to hidden data patterns and statistical information accumulated by organizations indicates that data mining is very valuable. The present book contains the fundamental concepts and methods of data mining and is organized into 5 chapters. At the end of each chapter, after giving a summary, the sources and references will be explained for further study. The first chapter is an introduction to the concepts of data mining. This chapter will include the book's vision and mission to get acquainted with the rest of the book. The second and third chapters are dedicated to the concepts of data mining and data preparation (cleaning and cleansing). Chapter 4 deals with online processing, data warehousing, and databases. In the fifth chapter, data mining methods including decision tree, clustering, classification, association rules, and other data mining algorithms are studied. The last chapter will address some of the issues related to data mining.