Preparation-related questions
Absolutely not! The WDC is designed for new or non-programmers. The reason why it's possible for anyone to learn a great deal of R in two days is because the R language is simpler and easier to understand than other general purpose programming languages. Good R programming can describe data the same way that we intuitively think about data, this make the logic behind the language easy to comprehend. As long as you feel comfortable using a computer, you will gain a lot from the course.

The course is centered around everyone coding on their own computers as we progress through theory and examples. There are only two things that are essential to bring:

  1. A laptop that can connect to wifi
  2. A power cord for your laptop

The programs used in the course support the following operating systems:

  • Windows
  • Mac OS X
  • Ubuntu
  • Fedora
R-related Questions

R is a free, open-source programming language that is designed primarily for data analysis, statistics, and graphics. The R language allows users to conduct sophisticated analysis and manipulations of data with often only a couple lines of code.

For more information, checkout this page on the Microsoft R Open community.

If you use any type of data to make decisions, knowing R will empower you with a vast set of tools that will take your analysis and decision-making to another level. In addition to becoming more efficient, work done in R will be more reliable, scalable, reusable, and reproducible than work done in spreadsheets.

The R language is free to download, and is becoming integrated with more and more products everyday. As the popularity of data science continues to surge, the benefits and value of knowing R will increase over time.

Due to R's growing popularity, many software providers offer R integrations. Here are some links to some popular R platforms and integrations:

The above is by no means a complete list - R is integrating with more applications all the time. In addition to software integrations, R can interface with most other popular programming languages including: python, C++, and java.
Course-related Questions

The course is a combination of theory and practice. We use slides to present theory and then we write code to solidify our knowledge.

For more details, please see the course curriculum.

At the end of this course you should be comfortable performing the following tasks:

  • Writing scripts to import and manipulate data in ways that would be extremely difficult or impossible to do in Excel.
  • Writing scripts to create powerful graphics objects that can quickly produce a wide range of visualizations.
  • Storing your procedures in custom functions that can used for future projects.
  • How to troubleshoot and debug your code.

The above items and the skills behind them represent the cornerstone of the R language. They also span the topics that people typically find the most difficult to self-learn. In short, you should feel confident using the R language to carry out analysis that is practical to your area of work.

Additionally, you will see examples (and receive example code) of the below categories:

  • Machine learning algorithms
  • R-based web application development
  • Creating dynamic reports and presentations
  • Overlaying data onto geographical maps
The course curriculum can be found here.

Since it's impossible to teach everything about R in a single weekend, we provide "maps" that will help guide you in your next steps to learning more about:

  • Machine learning algorithms
  • R-based web application development
  • Creating dynamic reports and presentations
  • Overlaying data onto geographical maps
Note: We will work through examples of the above categories during the course; however, each one is its own field of study. The maps we provide will help guide you to the right approach and the best libraries to use for each topic.