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  Photo by  Imat Bagja Gumilar  on  Unsplash I started learning data science as an environmentalist. Statistics was and will always be my first go-to tool to organize data for solving real-life problems. I studied a branch of environmental science that rarely anyone could ever think of as their first option to enter university. I studied forest and agricultural science. It is an interdisciplinary subject because I could focus not only on the forest, but also on plant physiology, genetics, ecology and landscape science, environmental science, epidemiology, and many more. Then again, I would love to talk about forestry and how actually broad the topic is despite a very narrow intuition that the name may bring, but in this post, I would like to talk about why environmentalists, and everyone, should learn to program. Personal documentation (Nancy, France, 2019). The MRI scan of wood, taken in the Wood Material Lab at INRAe Champenoux, Nancy I started learning to program ...

  Photo by Dose Media on Unsplash Correlation is one of the statistics’ all-time classics, yet it is still a busy measure that everyone uses in their analysis process. In classic interpretation, correlation is a measure of relationship or correspondence between two variables. This is usually visualized through a correlation plot and measured using correlation coefficient (r) that ranges between -1 to 1. The important takeaway from r is it shows the degree of relationship between two variables in terms of how a change in one variable will lead to a change in the corresponding variable. While interpreting the correlation plot is widely known and quite straightforward, the intuitive understanding of the equation of correlation coefficient (r) is less widely known. This is what the article is about. Why does the result be between -1 and 1; and where do the signs come from. Before we continue to the explanation about r, let’s recall the definition of correlation. Correlation: the relati...