"Glassdoor Pay Gap"

Leonardo Cavalcante Araújo

Data Analytics Full-Time FEB2021, Paris & March 2021

Content

• Project Description
• Objectives
• Workflow
• Organization
• Links

Project Description

Individual project developed in an afternoon, using a Glassdoor database found in Kaggle website.

Objectives

The project had 2 distinct objectives:

1. Derive statistically significant insights from a database.
2. Model a regression analysis for a variable (in this project, we have chosen to do use the linear regression to predict the probability of a crime to happen in a given date with some given circunstances.)

Workflow

1. Database search and download, finally deciding on a open source database found in this Kaggle link.
2. Data Exploration and Cleaning.
3. Data Analysis & Visualisations: Using Python, Matplotlib and Seaborn.
4. Hypothesis Testing: to test statistically significant events.

Next steps to be developed:

1. Linear Regression using OLS (Ordinary Least Squares): find a good variable to predict, maybe the salary.
2. Assumptions testing: verification of the assumptions for the OLS model.
3. Presentation: Google Slides construction.

Organization

• Repository "https://github.com/leo-cavalcante/glassdoor-pay-gap": you may find the main Python Notebooks produced by the team members to realize the analysis, visualisations and predictive models.
• Individual project.

Links

Here you may find the relevant links for the main documents produced during this project:

GitHub Repository: glassdoor-pay-gap

Glassdoor - Gender Pay Gap Analysis