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2019-03-25 20:24 #

What a beautiful artistic work at the crossroads between dataviz and infographics, by @janezhgw. #dataviz

2019-03-25 20:08 #

  Jazz Chill.

2019-03-25 18:03 #

  Nick Cave & The Bad Seeds, Nocturama.

2019-03-25 07:19 #

Here is the fourth edition of Algorithms, by Sedgewick & Wayne, a definitive book to have after Knuth’s monumental work and the Cormen et al. (via @TechSparx)

2019-03-22 12:20 #

Scientists rise up against statistical significance. Together with Moving to a World Beyond “p < 0.05”, it is probably time to rethink statistical significance and embrace the world of uncertainty instead. As Stephen Seen once said:

We can predict nothing with certainty but we can predict how uncertain our predictions will be, on average that is. Statistics is the science that tells us how.

2019-03-22 10:26 #

An Introduction to Applied Bioinformatics: An interesting online textbook that I found while browsing the scikit-bio Python package on Github. #python

2019-03-22 10:11 #

Interesting to know: The wakefield R packages allows to quickly generate random data sets. I learned about that while reading David Gohel’s Using R as a BI tool. #rstats

2019-03-21 21:42 #

On the simplicity of working with a Terminal: processing 44K of mails in less than 2 seconds.

2019-03-21 21:34 #

Too late to start re-reading Don Knuth’s excellent book on Mathematical Writing (PDF), but I will definitively do it in a few days.

2019-03-21 21:32 #

What is Data Science after all? I never liked this term, and I consider myself as a statistician, or better a data craftsman, because I mostly spend my time dealing with data after all. Stephanie C. Hicks & Roger D. Peng wrote a nice article, Elements and Principles of Data Analysis, which I believe provides quite an honest account of DS-related stuff:

Data science is the science and design of (1) actively creating a question to inves- tigate a hypothesis with data, (2) connecting that question with the collection of appro- priate data and the application of appropriate methods, algorithms, computational tools or languages in a data analysis, and (3) communicating and making decisions based on new or already established knowledge derived from the data and data analysis.