Requirements: Basic knowledge of mathematics, statistics and databases.
To have the necessary knowledge to enable data analysis, to understand the concept of trend, relationship, transformation among others used in advanced analytical models. The topics are covered from an intuitive perspective, using theory as a tool, starting from the understanding of the basic concept. It is not a substitute for complete courses on the topics mentioned here, but a review of the same that allows to identify those that require to be reviewed in depth by each attendee, previous knowledge of mathematics is required, without asking to remember in detail the above mentioned, since the review will be done during the course. It introduces the attendees to the development cycle of CRISP-DM analytics projects, as well as some basic algorithms, with the concept that originates them. It enables the formal execution of analytics projects, through the understanding of the cyclic nature of them, exemplifying in RapidMiner the application of the cycles for a clear understanding of the reason for them. Understanding the concept of correlation will enable the definition of redundant data as well as data that add value to the model and the application of simple classificatory models. It continues with the conceptualization process based on some analytical processes and gives a brief introduction to one of the most important models in the birth of artificial intelligence: the perceptron, which, although it has an extremely limited application, allows understanding the concept of "Machine Learning."