E-learning platforms and Massive Open Online Courses (MOOCs) are picking up momentum in popularity and are an emerging new model for education and training that delivers videotaped lectures and other course materials over the Internet for students and scientists at many different levels. These novel educational technologies do not only represent tools for learning, but they have the potential to become a catalyst for changing the whole education system.
In this issue we introduce selected MOOCs that focus on the statistical analysis of experiments and data and introduce important tools and mathematical concepts:

Coursera: Mathematical Biostatistics Boot Camp 1
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level. A small amount of linear algebra and programming are useful for the class, but not required.
edX: Introduction to Applied Biostatistics: Statistics for Medical Research
This Applied Biostatistics course provides an introduction to important topics in medical statistical concepts and reasoning. Each topic will be introduced with examples from published clinical research papers; and all homework assignments will expose learner to hands-on data analysis using real-life datasets. This course also represents an introduction to basic epidemiological concepts covering study designs and sample size computation. Open-source, easy-to-use software will be used such as R Commander and PS sample size software.
DataCamp: Introduction to R
In this introduction to R, the basics of this open source language, including factors, lists and data frames will be introduced. With the knowledge gained in this course, it will be possible to undertake the first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities.
edX: Data Analysis for Life Sciences 1: Statistics and R
This course covers the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. R programming examples are provided in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. Visualization techniques will be used to explore new data sets and determine the most appropriate approach. Robust statistical techniques are described as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, the basics of conducting reproducible research can be learned.