Nicolas Petit : Analyste de Données-data analysis insights and models.

Example

Performing a comprehensive sales analysis for an e-commerce business to identify trends, outliers, and growth opportunities.

Scenario

A retail company needs to understand the performance of its online store compared to physical locations. Nicolas applies statistical techniques to break down the revenue streams, identifying that online sales during specific holidays contribute to 35% of the overall revenue. By visualizing the trends, he reveals underperforming product categories that should be improved or removed, and recommends product bundles based on historical customer behavior.

Example

Creating a predictive model for customer churn using machine learning algorithms such as Random Forest or XGBoost.

Scenario

A telecommunications company wants to predict which of its customers are likely to switch to a competitor. Nicolas gathers customer data, including demographic, service usage, and interaction history, then builds a churn prediction model. His model achieves a 90% accuracy rate, allowing the company to identify at-risk customers and implement targeted retention campaigns. The result is a significant reduction in churn rate by 15% in the following quarter.

Example

Building dynamic dashboards for real-time monitoring of key business metrics using tools like Tableau or Power BI.

Scenario

A healthcare provider requires real-time tracking of patient admissions, discharge rates, and resource allocation across multiple facilities. Nicolas builds a live dashboard that pulls data from various hospital management systems, displaying key metrics such as bed occupancy rates, patient wait times, and available medical resources. This visualization allows hospital administrators to make quick, data-driven decisions to optimize patient flow and allocate resources where needed most.