Reports from an adventurous learning journey (Part 1)

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.


Beginning with December – when in a blog entry I recommended a book on Machine Learning with R – I initiated a new personal enterprise: Learning the statistical programming language R to acquire competencies of a data scientist. With "learning enterprise" I do not mean to get just interested in a new subject, to read from time to time a relevant book and to look into some web-based tutorials. No, with "enterprise" I mean a much bigger undertaking, to focus and concentrate several months on a systematic study for a new set of qualifications. 


I started with a nine-course introduction to data science, taught by three professors of  John Hopkins University and offered via the MOOC-platform Coursera. After I finished successfully two courses with certificates I turned impatient with teaching style and course philosophy.  My dissatisfaction was not so much caused by these professors or by this special course design but had more abstract reasons: As an experienced adult learner with a very attractive job I had no need to provide evidence of some acquired general qualifications as most of the course participants did. I was rather looking for some kind of Personal Knowledge which I could combine with my own life and working experiences.  After a long time again in the learner role, I found it very strange that I had to spend much time on material arranged by other people which I  could not need and had, on the other hand, to learn material superficially or to skip altogether, which was very useful for me. 


To exemplify the situation more in detail: In the third course on Getting and Cleaning Data we had to learn different ways to get data into the R environment: To download it with URLs, to scrap it from web pages, to load it from Excel sheets, to collect it via an API. But – for instance – we didn't learn how to get data from an SQL database. I appreciate that all these different methods are potentially important and as a data scientist one should know how to apply them. I also understand that it is impossible to learn all the different cases in detail. But I disagree that the best teaching method is an exemplary journey through some of these potentially important methods. A teaching strategy which would have better suited me would haven been a systematic and complete list of all methods with their pointers to the relevant R packages and supplemented with some prototypical program snippets to get started with. After getting a systematic overview and helpful material for future usage, one should have the opportunity to choose two or three methods and learn the nitty-gritty in practical assignments.

How to support the self-determined learner?

Consequently, I stopped my course attendance and started to look for tutorials, courses and other learning material on my own. I have to confess that I didn't know how exciting but also how difficult this self-paced and self-determined learning approach was respectively is. Between us educators, we are talking routinely about self-determined learning , but it is quite a different thing to experience related problems yourself.

On a non-regular basis, I will share my experiences from this adventurous learning journey in a series of blog comments. This was the first one.


Deci, E. L., & Ryan, R. M. (2008). Handbook of Self-Determination Research.
Ryan, R. M., & Deci, E. L. (2008). From Ego Depletion to Vitality: Theory and Findings Concerning the Facilitation of Energy Available to the Self. Social and Personality Psychology Compass, 2(2), 702–717.

Von Peter Baumgartner

Seit mehr als 30 Jahren treiben mich die Themen eLearning/Blended Learning und (Hochschul)-Didaktik um. Als Universitätsprofessor hat sich dieses Interesse in 13 Bücher, knapp über 200 Artikel und 20 betreuten Dissertationen niedergeschlagen. Jetzt in der Pension beschäftige ich mich zunehmend auch mit Open Science und Data Science Education.

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