datascience

Data visualization with Python

Project Description

Using both matplotlib and seaborn, we’ll learn how to create visualizations that are presentation-ready. The ability to present and discuss data with non-technical audiences is one of the most important facets of being a succesful Data Scientist. An immensely useful tool for enabling our ability to communicate insights is data visualization, which sits at the intersection of statistics (or more broadly data analysis) and design.

• In this project, we will be learning how to:
  • Create various types of plots, including bar-plots, distribution plots, box-plots and more using seaborn and matplotlib.
  • Format and stylize your visualizations to make them report-ready.
  • Create sub-plots to create clearer visualizations and supercharge your workflow.

The dataset is a CSV file named employee_churn_trimmed.csv, which contains data on employees who quit or stayed at their jobs. The purpose of this project is to uncover what could be driving churn and present findings in aeshtetically well made plots.