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.
Why Take This Course?
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.
Lesson 1: What is EDA? (1 hour)
We'll start by learn about what exploratory data analysis (EDA) is and why it is important. You'll meet the amazing instructors for the course and find out about the course structure and final project.
Lesson 2: R Basics (3 hours)
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. In this lesson, 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 (4 hours)
We 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 as we begin to make sense of a pseudo-data set of Facebook users. While the data set does not contain real user data, it does contain a wealth of information. Through the lesson, we will create histograms and boxplots, transform variables, and examine tradeoffs in visualizations.
Problem Set 3 (2 hours)
Lesson 4: Explore Two Variables (4 hours)
EDA allows us to identify the most important variables and relationships within a data set before building predictive models. In this lesson, we will learn techniques for exploring the relationship between any two variables in a data set. We'll create scatter plots, calculate correlations, and investigate conditional means.
Problem Set 4 (2 hours)
Lesson 5: Explore Many Variables (4 hours)
Data sets can be complex. In this lesson, we will learn powerful methods and visualizations for examining relationships among multiple variables. We'll learn how to reshape data frames and how to use aesthetics like color and shape to uncover more information. Extending our knowledge of previous plots, we'll continue to build intuition around the Facebook data set and explore some new data sets as well.
Problem Set 5 (2 hours)
Lesson 6: Diamonds and Price Predictions (2 hours)
Investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing. He'll recap many of the strategies covered in the course and show 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.
Final Project (10+ hours)
You've explored simulated Facebook user data and the diamonds data set. Now, it's your turn to conduct your own exploratory data analysis. Choose one data set to explore (one provided by Udacity or your own) and create a RMD file that uncovers the patterns, anomalies and relationships of the data set.
Pravin Mhaske completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
This 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 that's...
This 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 that's what it took me to complete it with 2-3 hours a day.
About the course -
If you are new to R, it will not teach you the ABC of it, but believe me I never felt the need of it though the only programming language I knew was COBOL. It is a primarily an analytics course and gets one well versed with the Data Analytics concepts and uses R as a tool to do that. The name it apt - Exploratory Data Analysis "using R"! The questions are interesting, having "no right or wrong answer" (as they keep saying :), and will make you think.
1. The course directly takes you to the next level of R without knowing much about it and you learn it well. After completing his course, I took the Coursera's R programming to learn the basics of the language. The basic difference was Coursera course is to be an R programmer while Udacity's course revolves around Data analytics, playing with data, visualization, a bit of statistics.
2. Plots and visualizations was never so much of fun. Few days after this course, I read somewhere that teaching R should be started with ggplot and I very much agree.
3. The course is self-paced, has tiny modules and heap of quizzes in between which really helps understand the concepts.
I've become a fan of Udacity and have done multiple course around statistics after this.
Life is Study
Life is Study completed this course.
The 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, transforming...
The 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, transforming data, correlation and linear regression, almost all of the quizzes and homework questions have to do with creating plots and making observations based on plots. This is not necessarily a bad thing--learning to plot in R is a valuable skill and an important part of exploratory data analysis--but it seems like the course should have spent a bit more time covering high-level concepts and numeric methods for exploring data like using tables and summaries. Despite that quibble, this is good course with a lot of high quality and practical content. It moves slowly enough for you to get comfortable with basic potting syntax before building up to more complex visualizations, but fast enough to keep you engaged.
Anonymous completed this course.
Very enjoyable class and I learned a lot. If you are new to R and are intimidated by the GGPlot2 package, this is for you.
Joe Foley is taking this course right now, spending 8 hours a week on it and found the course difficulty to be medium.
I 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 that...
I 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 that experience? Here are SOME of the improvements they have made while I have been enrolled. Initially one could get one-on-one help but usually it was 1 to 2 days out but at least was a video chat.
This was great. I had tried a competitor's course and sometime s one just cannot figure out why something is not working. But not with Udacity. Then they scrapped that and instituted a MENTOR program. Here one could instant message someone who would get back to you in a few hours. Then they scrapped that and now offer LIVE HELP. It is a chat box that one types the gist of your question into. In less than 10 min, often in 3 min , someone comes on. Usually they can immediately figure out your mistake ( it seems students make a finite # of errors) but if they cant they ask you to copy and paste your code. And if they still cannot figure it out, i.e., if you have really made a mess of things they do a screen sharing session to get you back on the rails . Don't make a mistake. Just sign up for Udacity.