Comprehensive Video Tutorials

Introduction to Data Science with R Training Video

CareerVision Training

£ 154 - ($ 597.170)

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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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¿Qué aprendes en este curso?

Data science
R video

Programa académico

Introduction to Data Science with R Training Video

  • Duration: 8.5 hours - 48 tutorial videos
  • Date Released: 2015-06-04
  • 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 Introduction to Data Science 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 Introduction to Data Science 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 Introduction to Data Science 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 Introduction to Data Science with R tutorial, online or offline... so you'll know the exact steps for your own projects.

Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It's ideal whether you're a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel.

RStudio Master Instructor Garrett Grolemund covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. You'll learn R's syntax and grammar as well as how to load, save, and transform data, generate beautiful graphs, and fit statistical models to the data. All of the techniques introduced in this video are motivated by real problems that involved real datasets. You'll get plenty of hands-on experience with R (and not just hear about it!), and lots of help if you get stuck.

Garrett Grolemund is a statistician, teacher, and R developer who works as a data scientist and Master Instructor at RStudio. He's conducted corporate training in R at Google, eBay, Axciom, and many other companies, and is currently developing a training curriculum for RStudio. Garrett co-authored the lubridate R package and wrote the ggsubplot package. He received his Ph.D at Rice University.

Course outline
01. Introduction To Data Science With R Introduction To The Course 02. The R Language Orientation To R Data Structures And Types Lists And Data Frames 03. The R Language 0301 Subsetting - Part 1 0302 Subsetting - Part 2 0303 R Packages 0304 Logical Tests 0305 Missing Values 04. Visualizing Data 0401 Introduction To ggplot2 0402 Aesthetics 0403 Facetting 0404 Geoms 0405 Position Adjustments 0406 Visualizing Distributions 0407 Visualizing Big Data 0408 Saving Graphs 05. Adjusting Graphs 0501 Visualizing Map Data 0502 Titles And Coordinate Systems 0503 Scales And Color Schemes 0504 Themes 0505 Axis Labels And Legends 0506 Further Learning 06. Tidy Data 0601 Reading In Data 0602 Melt 0603 dcast 0604 rbind And cbind 0605 Saving Data 07. Transforming Data 0701 Line Plots 0702 Filter And Select 0703 Arrange, Mutate, And Summarize 0704 Joining Data Sets 0705 Grouping Data 0706 The tbl Format 0707 Advanced Manipulations 08. Modeling Basics 0801 Introduction To Modeling 0802 Linear Models And Model Syntax 0803 Model Inference 0804 Categorical Variables 0805 Multivariate Models 09. Advanced Modeling 0901 Introduction To Variable Selection 0902 Best Subsets Selection 0903 Stepwise Selection 0904 Penalized Regression 0905 Non-Linear Models 0906 Logistic Regression 0907 Modeling Resources 10. Further Learning 1001 Resources For R