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Learning Path: Data Science with R

    • Learning to Program with R, by Stuart Greenlee

      To download the example code for this course, Click Here

      Get started on your path by learning how to install and navigate R, then tackle basic operations like statistical functions, matrix operations, and string functions. As you work through this course, you'll pick up everything you need to use R for developing statistical software and data analysis tools.

    • 04:18:15

    • Introduction to Data Science with R, by Garrett Grolemund

      To download the example code, Click Here

      Learn 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. Get lots of hands-on experience as you learn how to load, save, and transform data, generate beautiful graphs, and fit statistical models to the data.

    • 08:36:40

    • Expert Data Wrangling with R, by Garrett Grolemund

      Analysts often spend 50-80% of their time preparing and transforming data sets before they begin more formal analysis work. In this segment of the Learning Path, you'll learn how R and its packages can help you save time and tackle three main issues: data manipulation, data tidying, and data visualization.

    • 03:50:39

      • The dplyr Package

        Describe the dplyr package, what it does, and how to download it. Outline the chapter and pose the problem we will solve in the case study.
      • 00:02:23

      • Case Study 1 - TB Counts

        Use select(), filter(), mutate(), summarize(), group_by() and %>% to solve our initial question.
      • 00:08:26

      • Case Study 2 - TB Rates

        Summarize tidy data. Use gather() and spread() to reshape population data. Join() and mutate() to calculate rates.
      • 00:09:08

    • Writing Great R Code, by Richard Cotton

      Modern data analysis requires that you have two jobs: being a statistician and being a programmer. This is especially true with R. Fortunately, the jump from "writing code like a statistician" to "being a statistical programmer" isn't that far. This course guides you through a few simple skills that will vastly improve the quality of your code.

    • 00:59:13

    • Data Science with Microsoft Azure and R, by Stephen Elston

      This next segment of your Learning Path teaches you how to develop and deploy effective machine learning models in the Microsoft Azure Machine Learning (ML) environment. Start with an overview of Azure ML, and then learn to apply your R skills to create your own ML models.

    • 06:48:46

Learning Path: Data Science with R

  • Publisher: O'Reilly Media
  • Released: August 2015
  • Run time: 24 hours 34 minutes

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master all the features you'll need as a data scientist, from the basics to more advanced techniques including R Graph and machine learning. You'll work your data like never before.