Issue #123
July 4, 2021
This weeks Data Science Book is " Fundamentals of Data visualization " by C. O. Wilke. Here at D4Sci we're firm believers that an image is worth 1000 words (check out V4Sci if you haven't already) and this book will quickly and easily super charge your visualization skills. Wilke brings to bear the perspective of a Data Scientist to a field often dominated by Designers. Throughout this book, Wilke teaches you how to craft powerful visualizations with your tool or programming language of choice by introducing only as much visualization theory and concepts as necessary and without getting lost in abstract ideas or concepts. The authors experience as a Data Science makes this one of the best applied books on Visualization, despite the fact that the book is entirely language agnostic.
- 1. Understanding AWK [earthly.dev]
- 2. The Great AI Reckoning [spectrum.ieee.org]
- 3. Statistics as algorithmic summarization [argmin.net]
- 4. What John Von Neumann Really Did At Los Alamos [3quarksdaily.com]
- 5. Category Theory Illustrated [boris-marinov.github.io]
- 6. The Next Big Thing? Go Back To The Future [blog.eutopian.io]
- 7. Gentle introduction to GPUs inner workings [vksegfault.github.io]
- • Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics (L. Dyson, E. M. Hill, S. Moore, J. Curran-Sebastian, M. J. Tildesley, K. A. Lythgoe, T. House, L. Pellis, M. J. Keeling)
- • A distributed Monte Carlo based linear algebra solver applied to the analysis of large complex networks (F. Magalhães, J. Monteiro, J. A. Acebrón, J. R. Herrero)
- • Detecting informative higher-order interactions in statistically validated hypergraphs (F. Musciotto, F. Battiston, R. N. Mantegna)
- • Characterizing User Susceptibility to COVID-19 Misinformation on Twitter (X. Teng, Y.-R. Lin, W.-T. Chung, A. Li, A. Kovashka)
- • Systemic risk in interbank networks: disentangling balance sheets and network effects (A. Ferracci, G. Cimini)
- • Infrastructures connecting people. A mechanistic model for terrestrial transportation networks (L. Prignano, L. Font-Pomarol, I. Morer, S. Lozano)
- • Neural Distance Embeddings for Biological Sequences (G. Corso, R. Ying, M. Pándy, P. Veličković, J. Leskovec, P. Liò)
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