Data Analysis with R -

Overview

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by John Tukey, exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods.

If you’re interested in supplemental reading material for the course check out the Exploratory Data Analysis book. (Not Required)

This course is also a part of our Data Analyst Nanodegree.

Syllabus

What is EDA?
- Start by learn about what exploratory data analysis (EDA) is and why it is important.
R Basics
- EDA, which comes before formal hypothesis testing and modeling, makes use of visual methods to analyze and summarize data sets.,R will be our tool for generating those visuals and conducting analyses.,We will install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
Explore One Variable
- Perform EDA to 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 to make sense of a pseudo-data set of Facebook users.,Create histograms and boxplots, transform variables, and examine tradeoffs in visualizations.
Explore Two Variables
- DA allows us to identify the most important variables and relationships within a data set before building predictive models.,Learn techniques for exploring the relationship between any two variables in a data set.,Create scatter plots, calculate correlations, and investigate conditional means.
Explore Many Variables
- Learn powerful methods and visualizations for examining relationships among multiple variables.,Reshape data frames and how to use aesthetics like color and shape to uncover more information,Continue to build intuition around the Facebook data set and explore some new data sets as well.
Diamonds and Price Predictions
- Investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing.,See how predictive modeling can allow us to determine a good price for a diamond.,As a final project, you will create your own exploratory data analysis on a data set of your choice.

Taught by

Moira Burke and Dean Eckles

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Reviews

4.6 rating, based on 18 Class Central reviews

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Pravin Mhaske 6 years agoThis was the first course I took since I started thinking about analytics and R. A fellow Data Scientist recommended it to me. I was bit surprised when I saw the level as Intermediate still decided to pursue. Duration of the course is 2 months and t… Read moreHelpful
LSLife is Study 9 years agoThe course provides an overview of using R to explore data and focuses heavily on the use of the ggplot2 package in R to create data visualizations. Although the course touches briefly on high-level theory and concepts like summary statistics, trans… Read moreHelpful
AAAnonymous 9 years agoVery enjoyable class and I learned a lot. If you are new to R and are intimidated by the GGPlot2 package, this is for you.Helpful
Joe Foley 6 years agoI was skeptical when I enrolled in UDACITY’s Data Analysis Nano Degree Program but not only have they provided the experience they said they would they have steadily made improvements since I enrolled. How many times in your life have you had tha… Read moreHelpful
Armand Del Olmo 7 years agoIf you are looking for a good start on the topic of data visualization in R. This is the best choice in the web.Helpful
AAAnonymous 9 years agoOK course, very short.Mostly goes over how to plot in R, except for the final week which is very interesting.Helpful
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