Data Analysis with R

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9/10 stars
based on  3 reviews
Provided by:
Cost FREE
Start Date On demand

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Udacity online courses
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and ...
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and earn a verified certificate at the end of the course as proof of all that you've learned. You'll be learning from knowledgeable professors across various schools and parts of the globe. Learn about computer science from Dave Evans, an instructor at the University of Virginia, or delve into app development with Samantha Ready, a Developer Evangelist at Salesforce.com.

Provider Subject Specialization
Sciences & Technology
102 reviews

Course Description

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by [John Tukey](http://en.wikipedia.org/wiki/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.
Data Analysis with R course image
Reviews 9/10 stars
3 Reviews for Data Analysis with R

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Greg Hamel profile image
Greg Hamel profile image
9/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 5 months ago
Exploratory data analysis is the third course released as a part of Udacity's new Data science focus area that launched at the beginning of 2014. 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 conte... Exploratory data analysis is the third course released as a part of Udacity's new Data science focus area that launched at the beginning of 2014. 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. Be aware that the course mainly uses two data sets to teach the material: a data set of diamond prices and characteristics and set of pseudo Facebook data created by the instructors meant to mirror real Facebook data, such as friend counts, tenure on the site, user age and gender. Your enjoyment of the class will depend, in part, on your interest in the data.
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Heonkyu Jin profile image
Heonkyu Jin profile image
9/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 7 months ago
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Kevin Zhu profile image
Kevin Zhu profile image
10/10 starsCompleted
  • 7 reviews
  • 5 completed
5 years, 4 months ago
after I finish the course I will post suggestions. You will learn a lot about ggplot and some other package.
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