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Course Description
R is a free software environment for statistical computing and graphics (www.r-project.org). It can effectively analyze large-scale datasets, such as those resulting from high-throughput sequencing experiments. It promotes automated and reproducible analyses of scientific data, creates a wide spectrum of publication quality figures, and has an extensive library of add-on packages to facilitate many complex statistical analyses. Much of the power of the R platform derives from the fact that it is NOT a point-and-click environment, and thus has a steeper than usual learning curve. This is compounded by the reality that much of the documentation for R is geared toward those with very advanced training in statistics. This hands-on workshop will help you overcome these obstacles by providing participants with the knowledge and experience to use R for practical scientific data management and analysis applications. This workshop will also demonstrate principles and best practices for handling scientific data at scale, which is becoming ever more important in the ‘big data’ era.
- 1
Introduction
FreeGetting comfortable with the Console and Script windows, using R's calculator functionality
- 2
Data Structures
FreeIntroduces the different types of data structures
- 3
Plotting
FreeIntroduces the ggplot2 package
- 4
Data wrangling
FreeIntroduces split-apply-combine
Collaborators
Paul Zumbo
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Louis Maiden
Harvard Business School
DataCamp is by far my favorite website to learn from.
Ronald Bowers
Decision Science Analytics, USAA
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Create Your Free Account
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).