Free Course
Data Analysis with R
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Visually Analyze and Summarize Data Sets
Nanodegree Program
Data Analyst
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Become a Data Analyst
Accelerate your career with the credential that fast-tracks you to job success.
About this Course
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.
Course Cost
Free
Timeline
Approx. 2 months
Skill Level
intermediate
Included in Product
Rich Learning Content
Interactive Quizzes
Taught by Industry Pros
Self-Paced Learning
Student Support Community
Join the Path to Greatness
This course is your first step towards a new career with the Data Analyst Program.
Free Course
Data Analysis with R
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Enhance your skill set and boost your hirability through innovative, independent learning.
Course Leads
Moira Burke
Instructor
Chris Saden
Instructor
Solomon Messing
Instructor
Dean Eckles
Instructor
What You Will Learn
lesson 1
What is EDA?
- Start by learn about what exploratory data analysis (EDA) is and why it is important.
lesson 2
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.
lesson 3
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.
lesson 4
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.
lesson 5
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.
lesson 6
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.
Prerequisites and Requirements
A background in statistics is helpful but not required. Consider taking Intro to Descriptive Statistics prior to taking this course. Relevant topics include:
- Mean, median, mode
- Normal, uniform, and skewed distributions
- Histograms and box plots
Familiarity with the following CS and Math topics will help students:
- Variable assignment
- Comparison and logical operators ( <, >, <=, >=, ==, &, | )
- If else statements
- Square roots, logarithms, and exponentials
See the Technology Requirements for using Udacity.
Why Take This Course
You will...
- Understand data analysis via EDA as a journey and a way to explore data
- Explore data at multiple levels using appropriate visualizations
- Acquire statistical knowledge for summarizing data
- Demonstrate curiosity and skepticism when performing data analysis
- Develop intuition around a data set and understand how the data was generated.