A continuación, le indicamos cómo puede optimizar los proyectos y equipos de minería de datos a través de la retroalimentación y la evaluación.

Having clear project objectives is the foundation for its success. Objectives that meet the SMART specifications are the basis for being able to work in a way that fits the client's needs and meets their requirements, which guarantees the success of the operation.

Data mining, data science, and AI project needs an iterative process. The failures is not a failure like the waterflow project. The important things is to make a good iterative process and off course the feedback from user is the best resources for make that iterative improvement process.

Team dynamics maximize the possibility of project success by distributing responsibilities and optimizing efforts, as a result of leveraging the key competencies of each team member and the synergies developed among them. If a project has deadlines, a coherent team is the optimal way to carry it out.

Data is the raw material of analysis and data mining. It may be that the necessary information is not present in the data, and it is even possible that the data in our information repository are irrelevant to the purposes of the investigation. Well, this is also an insight because we know that we need to look elsewhere. But if, on the contrary, our data are relevant to the set objectives, a meticulous treatment and the choice of the appropriate technique will bring to light the relationships within the data corpus and the magnitude of these relationships.

The most authentic analytical tools are Calculus and multivariable analysis. There are recursive procedures based on these disciplines that ensure success in the optimization tasks necessary for segmentation, clustering, prediction, classification, etc. Data mining also relies on open-source software to implement these operational techniques. The analyst must know the theoretical foundations and the necessary programming tools to successfully implement the different components that make up their analytical corpus. Additionally, they must be able to set the conditions for the analysis and explain the courses of action that emerge from their results.

Claro, aquí tienes la traducción al inglés del nuevo fragmento: Experience is a variable that logically should influence the salary received by a data mining analyst. Just as with the different types of roles, a statistical analysis can be done by experience ranges: less than one year, between 1 and 2 years, from 2 to 5 years, from 5 to 10 years, and more than 10 years. This factor can be isolated or treated as a variable of interest, just like the different types of roles.