These are some of the Data Science and Machine Learning techniques we apply to the available data


We use a wide spectrum of algorithms to model the problems to be solved. Some examples:

Neural Networks

Model based on imitating brain neurons. A set of units, called artificial neurons, are defined, connected to each other and the information goes through the neural network producing output values.

Cluster Analysis

Statistical models of grouping observations from the detection of similar behavior patterns

Genetic Algorithms

Modeling resulting from iterative processes from a set of restrictive conditions to the solution

SVM (Support-Vector Machine)

Supervised learning models used for classification and regression analysis

PCA (Principal Component Analysis)

Technique used in exploratory data analysis and predictive models.

Gradient Boosting

Model for regression and classification that produces a predictive model usually in the form of decision trees.


We use a wide variety of data analysis techniques. Among them we can highlight:

Data Visualization

Dashboards for reporting and data exploration

Feature Engineering

Establishment of new composite variables from raw data to be used as input for the models.

Data Augmentation

Data increase from variations on an existing data set

Data Enrichment

Data expansion, connection between the organization's own databases and open data or external providers databases.

Data Wrangling

Data hygiene and standardization for subsequent statistical treatment.

Subscribe to our newsletter