Data Science is the concept that encompasses all disciplines related to the study of data, from its extraction and analysis to its modelling and future predictions. It has come to be described as the fourth major branch of science after empirical, theoretical, and computational science.
Predictive Analytics focuses on techniques for learning from past and present data to make predictions about the future. This allows us to anticipate future business situations and to be able to influence them before they occur, with the benefit that this entails compared to only analysing the facts once they have already occurred.
The process is divided into several steps.
- Understanding and determination of the objective:
An essential step is to know what the underlying business objective is, align it with the analysis to be performed, and determine what the requirements are. - Data Ingestion and Exploration:
This step aims to collect, describe, and explain the data needed for the analysis using data-analysis tools and scientific techniques, such as statistics. - Preparation of the data:
Data preparation consists of organizing and debugging the different data sources. It is necessary to clean up the data to avoid having redundant data that could damage the performance of the Machine Learning models to be considered. - Creation of Machine Learning models:
By means of Machine Learning models it is possible to “model” the available data in order to use them in new situations and make future predictions. - Evaluation of the model:
It is reviewed with the client that the objectives have been met and, if necessary, we would return to the previous steps in order to meet expectations. - Integration:
The solution is implemented fully integrated with the customer’s systems.
Through Predictive Analytics we can make decisions in advance, before events occur, maximizing or minimizing their impact depending on whether it is a positive or negative impact.
Currently both Data Science and Predictive Analytics are being used in many sectors such as, for example, the financial sector, retail, insurance, health, telecommunications, IT security, or the energy sector.
