larryhenryjr/explore-and_summarize-data-project

Exploratory Data Analysis using R

Project: Explore and Summarize Data

As part of Udacity's Data Analysis Project, I will use R and apply exploratory data analysis techniques to explore relationships in one variable to multiple variables and to explore a selected data set for distributions, outliers, and anomalies.

I will be exploring the Prosper loan dataset (a p2p lending platform) with R and RStudio. The main purpose of this analysis is to explore the various factors that affect borrowers' rate. The data can be find here.

Project Files

• esd-project-file.rmd: R markdown for the analysis, plots and summary, and reflection
• esd-project-file.html: knitted html file
• Refereces.txt : List of Web sites used in creating my project file

Project Overview

Why this Project?
Exploratory Data Analysis (EDA) is the numerical and graphical examination of data characteristics and relationships before formal, rigorous statistical analyses are applied.

EDA can lead to insights, which may uncover to other questions, and eventually predictive models. It also is an important “line of defense” against bad data and is an opportunity to notice that your assumptions or intuitions about a data set are violated.

What will I learn?
After completing the project, you will:

• Understand the distribution of a variable and to check for anomalies and outliers
• Learn how to quantify and visualize individual variables within a data set by using appropriate plots such as scatter plots, histograms, bar charts, and box plots
• Explore variables to identify the most important variables and relationships within a data set before building predictive models; calculate correlations, and investigate conditional means
• Learn powerful methods and visualizations for examining relationships among multiple variables, such as reshaping data frames and using aesthetics like color and shape to uncover more information

Why is this Important to my Career?
"If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis"

— Hal Varian, UC Berkeley, Chief Economist at Google