Issue #114

July 4, 2021

This weeks Data Science Book is the recently released second edition of " Scientific Computing with Python " by C. Führer, O. Verdier and J. E. Solem which fills an important gap in most Data Scientists bookshelves. High performance computing, specially scientific and numeric computing, is a fairly technical branch of computer science that most programmers aren't really familiar with. It requires a deep understanding of the finer points of the data structures being used and of their specific implementations. With this book the authors have managed to demystify a highly complex subject with clear explanations and analysis that will appeal to developers of all levels. Naturally, some of the subjects could gain from being further developed, but in every project of this kind a balance must be struck between breath and depth. Overall, a useful book that I'll refer to often.

Scientific Computing with Python

1. Dimensionality reduction in neural data analysis [xcorr.net]
2. A.I. Predicts the Shapes of Molecules to Come [nytimes.com]
3. Hundreds of AI tools have been built to catch covid. None of them helped [technologyreview.com]
4. A blood marker predicts who gets ‘breakthrough’ COVID [nature.com]
5. The Consistent Estimator [towardsdatascience.com]
6. Learning to Extrapolate with Generative AI Models [blog.einstein.ai]
7. Python PDF Handling Tutorial [github.com/prajwollamichhane11]
8. A Natural Language Processing (NLP) Primer [towardsdatascience.com]
9. Building intuition for p-values and statistical significance [bytepawn.com]
10. Machine-learning on dirty data in Python: a tutorial [dirtydata.science]

• Historical language records reveal a surge of cognitive distortions in recent decades (J. Bollen, M. ten Thij, F. Breithaupt, A. T. J. Barron, L. A. Rutter, L. Lorenzo-Luaces, M. Scheffer)
• Mobility patterns are associated with experienced income segregation in large US cities (E. Moro, D. Calacci, X. Dong, A. Pentland)
• Privacy implications of accelerometer data: a review of possible inferences (J. L. Kröger, P. Raschke, T. R. Bhuiyan)
• How epidemic psychology works on Twitter: evolution of responses to the COVID-19 pandemic in the U.S. (L. M. Aiello, D. Quercia, K. Zhou, M. Constantinides, S. Šćepanović, S. Joglekar)
• Individual-driven versus interaction-driven burstiness in human dynamics: The case of Wikipedia edit history (J. Choi, T. Hiraoka, and H.-H. Jo)
• Geospatial Model of COVID-19 Spreading and Vaccination With Event Gillespie Algorithm (A. Temerev, L. Rozanova, J. Estill, O. Keiser)
• Hiding in Temporal Networks (M. Waniek, P. Holme, T. Rahwan)
• On node ranking in graphs (E. Dudkina, M. Bin, J. Breen, E. Crisostomi, P. Ferraro, S. Kirkland, J. Marecek, R. Murray-Smith, T. Parisini, L. Stone, S. Yilmaz, R. Shorten)

AWS Boto3 Python Crash Course with AWS S3

July 4, 2021

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