With December last year, I started to learn data science using R. As a self-determined learner, I had not only to find resources, that are adequate to my learning goals and status of competencies but also to plan and organize my spare time for my self-organized continuing education.This article reports about my strange learning behavior and closes with 3 tips on R learning resources for the beginner, suggesting the most minimal learning set for the prospective data scientist.
In the first episode about my personal learning experiences, I criticized that rigid curricula are not adequate to support experienced learner. This second episode is a follow up of this thought. I discuss the subject in more detail and in a more general way addressing not only R programming and data science but generally all adult learners who have work experiences and want to improve their knowledge and skills in their respective field of expertise. I will also present a model how self-determined learners could better be supported.
This is the first report of a series of personal experiences in learning the statistical programming language R to acquire competencies of a data scientist. In this kick-off article, I will present an example, why cohort learning in the age of individualisation is not appropriate anymore. I will suggest an alternative, present my earned certificates and explain why I interrupted (or aborted?) a nine-course introduction to data science taught by professors of John Hopkins University and offered via the MOOC-platform Coursera.