Issue #145
July 4, 2021
This weeks Data Science Book is " Causality " by J. Pearl. Causal Inference is a lively and fast developing area in Data Science that we believe has the potential to be truly revolutionary in coming years (you can get a quick overview of the main ideas in our Causal Inference series over at Medium). Judea Pearl is one of the most prominent founding fathers of this field that he introduces masterfully in this textbook. While the approach Pearl chooses is mathematically rigorous, thanks to his rich use of toy examples, the key ideas and concepts are easily grasped and adapted to real world datasets. Causal Inference is a powerful arrow in any Data Scientist's quiver and this is the ideal starting point if you're interested in taking the first steps in this exciting area.
- 1. Building a Machine Learning Web Application Using Flask [towardsdatascience.com]
- 2. 33 Data Visualization Techniques All Professionals Should Know [dipesious.medium.com]
- 3. The Inevitability of Trusted Third Parties [onezero.medium.com]
- 4. A Data-Driven Approach to Understanding How the Brain Works [hai.stanford.edu]
- 5. Time series clustering based on autocorrelation using Python [medium.com/wwblog]
- 6. 20 ideas for better data visualization [uxdesign.cc]
- 7. 7 Methods For Better Machine Learning [odsc.com/blog/]
- • In-degree centrality in a social network is linked to coordinated neural activity (E. C. Baek, R. Hyon, K. López, E. S. Finn, M. A. Porter, C. Parkinson)
- • Indirect influence in social networks as an induced percolation phenomenon (Jiarong Xie, Xiangrong Wang, Ling Feng, J.-H. Zhao, W. Liu, Y. Moreno, Y. Hu)
- • Brains and algorithms partially converge in natural language processing (C. Caucheteux, J.-R. King)
- • Population games on dynamic community networks (A. Govaert, L. Zino, E. Tegling)
- • Conversational Agents: Theory and Applications (M. Wahde, M. Virgolin)
- • Gradients without Backpropagation (A. G. Baydin, B. A. Pearlmutter, D. Syme, F. Wood, P. Torr)
- • Do We Really Need Deep Learning Models for Time Series Forecasting? (S. Elsayed, D. Thyssens, A. Rashed, H. S. Jomaa, L. Schmidt-Thieme)
Working with Audio Data in Python
All our videos are also available in our YouTube playlist.
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