Data science

Statistical analysis, Model development and deployment.

Tailor-made deliverables

Design of clear and innovative tailor-made deliverables

Up-to-date models and methods

Use of the most up-to-date and suitable statistical models and methods

Unlocking value of your data

Unlock the value hidden inside your data

Our team of Data Scientists helps organisations gain the most value from data by focusing on data mining using both classical statistical methods as well as machine learning models and deep learning.

We use our interdisciplinarity skills in statistics, coding and business to offer our customers end-to-end services with the highest quality standard.

We consider that each company has specific data science needs. Therefore, we deploy client-centric approaches through tailored services in order to efficiently meet all your requests and support your goals.

3.1 Statistical analysis

3.1
Statistical analysis
We perform a deep descriptive analysis of your data.

Our data scientists are used to find correlations and patterns among the dozens of variables in your databases. The aim is to analyze your data from different angles and present you the insights that will allow you to discover the untapped levers of your business. A main objective of the exploratory phasis is twofold.

First, select the key explanatory variables of your business.

Second, identify the variable(s) that should be seen as outputs rather than inputs, because their value can be explained by the values taken by the other variables.

These output variables are directly available as such or can be constructed, a typical example being a key performance indicator.

Some example of tools we use : Correlation Matrices, PCA analysis, Statistical Tests, etc.

3.2 Model development

3.2
Model development
We model how the selected explanatory variables explain the output variable(s).

Modelling means that we highlight the sometimes complex interaction between all the explanatory variables and the output variable. In order to do so, we use both classical models as well as machine learning models or deep learning models, depending on which model best suits to explain the interaction.

Some example of tools we use : Linear Models, Generalized Linear Models, Generalized Additive Models, Random Forest, Gradient Boosting Method, Neural Networks, etc.

3.3 Model deployment, predictions and output

3.3
Model deployment, predictions and output
Modelling is good, business application of it is better !

Our consultants deploy data mining models where you need them either in a dedicated app or in your own product.

The objective is to provide visual analysis tools and data-driven dashboards to make the output clear and relevant in order to support human decisions.

This gives our clients a priceless competitive advantage, an improved understanding of core business issues and impressive cost optimization.

Some example of tools we use : R, R Shiny, R Markdown, Python, Videos, etc.