This program helps you learn how to organize data, discover patterns and insights, draw meaningful conclusions and clearly communicate critical results using Python and its data analysis libraries (Numpy, Pandas, Matplotlib) and SQL. Three required projects can be found here.
In this project I'm working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. The goal is to work through this notebook to help the company understand if they should implement this new page.
The mission in this project is to wrangle WeRateDogs Twitter dataset. The Twitter archive only contains very basic tweet information. Extending the data through other datasets is necessary to get all information. Additional gathering, then assessing and cleaning is required for analyses and visualizations.
In this latest project I conduct an exploratory data analysis on a dataset containing loans extracted from Prosper (America's first peer-to-peer lending marketplace). The analysis is structured, moving from simple univariate relationships, to bivariate relationships and multivariate relationships. Questions are addressed in order to make discoveries and draw conclusions.