Discover more about our new course “Data Visualization with ggplot2 - Part 2”, where you'll take the step from exploratory to explanatory data visualization.
This blog post will walk you through some basic examples of building Submission Correctness Tests (SCTs) so that you can begin to create your own interactive courses on DataCamp.
At DataCamp, we’ve been working on a improved way of creating content. DataCamp’s new Teach app is closely linked with GitHub, the popular web-service where you can collaborate on Git Repositories. To make creating and maintaining courses as easy as possible, the DataCamp Teach app can automatically convert a GitHub repository in a DataCamp course. This is similar to how continuous integration tools such as Travis CI and Jenkins work: simply push changes to your GitHub repository
Learn to produce data visualizations with DataCamp’s ggplot2 course series, following the principles of good visualizations and the grammar of graphics plotting concept.
Exploratory Data Analysis (EDA) is often the best starting point to any analysis. Take the new hands-on course from Kaggle & DataCamp to learn the essentials of Data Exploration.
Adding Python to your toolbox will boost your versatility and bring you closer to becoming a true data scientist, and DataCamp's new Intro to Python from Data Science tutorial is the perfect way of doing so.
Importing your data into R to start your analyses: it should be the easiest step. Unfortunately, this is almost never the case. Data is stored in all sorts of formats, ranging from from flat files to other statistical software files to databases and web data. A skilled data scientist knows which techniques to use to in order to proceed with the analysis of data.
This free introduction to R tutorial helps you master the basics of R. Become ready to undertake your own data analysis.