Expert Data Wrangling with R Training Video

CareerVision Training
In Birmingham (Grossbritannien)

£ 72 - (80 )
zzgl. MwSt.

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  • Kurs
  • Birmingham (Grossbritannien)

The following course, offered by Career vision, will help you improve your skills and achieve your professional goals. During the program you will study different subjects which are deemed to be useful for those who want to enhance their professional career. Sign up for more information!

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Careervision 61 Caroline Street,, B3 1UF, West Midlands, Grossbritannien

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Expert Data Wrangling with R Training Video

  • Duration: 4 hours - 22 tutorial videos
  • Date Released: 2015-06-02
  • Works on: Windows PC or Mac
  • Format: DVD and Download
  • Instructor: Garrett Grolemund

A Practical Training Course That Teaches Real World Skills

In this project-based Expert Data Wrangling with R video tutorial series, you'll quickly have relevant skills for real-world applications.

Follow along with our expert instructor in this training course to get:

  • Concise, informative and broadcast-quality Expert Data Wrangling with R training videos delivered to your desktop
  • The ability to learn at your own pace with our intuitive, easy-to-use interface
  • A quick grasp of even the most complex Expert Data Wrangling with R subjects because they're broken into simple, easy to follow tutorial videos

Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Expert Data Wrangling with R tutorial, online or offline... so you'll know the exact steps for your own projects.

Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. This video tutorial shows you how to streamline your code-and your thinking-by introducing a set of principles and R packages that make this work much faster and easier. Garrett Grolemund, Data Scientist and Master Instructor at RStudio, demonstrates how R and its packages help you tackle three main issues:

- Data Manipulation. Data sets contain more information than they display. By transforming your data, you can reveal a wealth of descriptive statistics, group level observations, and hidden variables. R's dplyr package provides optimized functions to help you transform data, as well as a pipe syntax that makes R code more concise and intuitive.

- Data Tidying. Data sets come in many formats, but R prefers just one. R runs quickly and intuitively when your data is stored in the tidy format, a layout that allows vectorized programming. R's tidyr package reshapes the layout of your data sets, making them tidy while preserving the relationships they contain.

- Data Visualization. The structure of data visualizations parallels the structure of data sets. Once your data is tidy, visualizations become straightforward: each observation in your dataset becomes a mark on a graph, each variable becomes a visual property of the marks. The result is a grammar of graphics that lets you create thousands of graphs. R's ggvis package implements the grammar, providing a system of data visualization for R.

Garrett Grolemund is a Data Scientist and Master Instructor at RStudio. Garrett maintains the lubridate R package and is the author of Hands-On Programming with R and the upcoming Data Science with R (both O'Reilly books).

Course Outline

01. Introduction Introduction Two New Conventions Data Science For Data Wranglers 02. Data Manipulation 0201 The dplyr Package 0202 Select Variables 0203 Filter Observations 0204 Derive Variables 0205 Summarize Observations 0206 Group Observations 0207 Re-Arrange Observations 0208 Case Study 1 - TB Counts 0209 Data Science For Data Wranglers - Units Of Analysis 03. Data Tidying 0301 Data Science For Data Wranglers - Tidy Data 0302 Reshape The Layout Of Your Data 0303 Separate And Unite Variables 0304 Data Science For Data Wranglers - The Best Format 0305 Combine Data Sets 0306 Case Study 2 - TB Rates 04. Data Visualization 0401 Data Science For Data Wranglers - The Structure Of Visualizations 0402 Visualize Observations 0403 Visualize Variables 05. Conclusion 0501 How To Learn More