The course Data Analysis with R is an online class provided by Udacity. The skill level of the course is Intermediate. It may be possible to receive a verified certification or use the course to prepare for a degree.
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.
Data Analysis with R
Visually Analyze and Summarize Data Sets
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.
What is EDA?
Explore One Variable
Explore Two Variables
Explore Many Variables
Diamonds and Price Predictions
Start by learn about what exploratory data analysis (EDA) is and why it is important.
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.
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.
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.
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.
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.
A background in statistics is helpful but not required. Consider taking Intro to Descriptive Statistics prior to taking this course. Relevant topics include:
Familiarity with the following CS and Math topics will help students:
See the Technology Requirements for using Udacity.
To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Here you can find information, reviews and user experiences for the course “Data Analysis with R“. The provider of the course – “Udacity” – will be glad to answer any questions you may have about the class, click here to use the offical support channels. It would be great if you could share your experience of participating in the course – Your honest review will surely help others to choose the right class!