In these courses, we introduce foundational statistical topics such as exploratory data analysis, statistical inference, and modeling with a focus on both the why and the how. We use real data examples to introduce the ideas of statistical inference within a randomization and simulation framework. We also walk students through the implementation of each method in R using tools from the tidyverse so that students completing the courses are equipped with both a conceptual understanding of the statistical methods presented and also concrete tools for applying them to data.
The time commitment (~4 hours) for each of the DataCamp courses is just long enough to really sink your teeth into a topic without having to commit to an entire semester. After taking a course, you will be in a position to move forward either to apply the topic to your own work or to take more courses in order to deepen your knowledge.
If you want to build your technical skills for data science, there are many resources online. What makes DataCamp special is the interactive coding environment that offers immediate feedback. This introductory statistics sequence goes even further by coordinating a sequence of courses around a single theme.