Issue #86
January 17, 2021
Our very first Data Science Book is " Data Science for Business " by Foster Provost and Tom Fawcett. This book has the distinction of having been the first book I ever read when I first started getting interested in Data Science back in the day. While it doesn't dive into the Data Science programming stack, Provost and Fawcett have a gift for putting the fundamental concepts and ideas of Data Science into a practical business context that makes it clear when each algorithm should be applied and where they might be found lacking.
- 1. Trusting machines versus humans. We must understand the difference [weforum.org]
- 2. We Don't Need Data Scientists, We Need Data Engineers [mihaileric.com]
- 3. Medicine's Machine Learning Problem [bostonreview.net]
- 4. SQLite as a document database [dgl.cx]
- 5. Machine Learning: The Great Stagnation [marksaroufim.substack.com]
- 6. Gaussian Process Regression [towardsdatascience.com]
- 7. Real World Applications of Markov Decision Process [towardsdatascience.com]
- 8. What is PCA? [medium.com/swlh]
- • COVID-19 measures also suppress flu—for now (K. Servick)
- • An evidence review of face masks against COVID-19 (J. Howard, A. Huang, Z. Li, Z. Tufekci, V. Zdimal, H.-M. van der Westhuizen, A. von Delft, A. Price, L. Fridman, L.-H. Tang, V. Tang, G. L. Watson, C. E. Bax, R. Shaikh, F. Questier, D. Hernandez, L. F. Chu, C. M. Ramirez, A. W. Rimoin)
- • Challenges when identifying migration from geo-located Twitter data (C. Armstrong, A. Poorthuis, M. Zook, D. Ruths, T. Soehl)
- • Mapping coupled time-series onto a complex network (J. Ardalankia, J. Askari, S. Sheykhali, E. Haven, G. R. Jafari)
- • Superintelligence cannot be contained: Lessons from Computability Theory (M. Alfonseca, M. Cebrian, A. F. Anta, L. Coviello, A. Abeliuk, I. Rahwan)
- • Convolutional Neural Nets: Foundations, Computations, and New Applications (S. Jiang, V. M. Zavala)
- • Privacy preserving data visualizations (D. Avraam, R. Wilson, O. Butters, T. Burton, C. Nicolaides, E. Jones, A. Boyd, P. Burton)
- • Graph embeddings for Abusive Language Detection (N. Cecillon, V. Labatut, R. Dufour, G. Linares)
Twitter Sentiment Analysis Using Python
All our videos are also available in our YouTube playlist.
Enjoy the newsletter?
Forward it to a friend, or subscribe to get it straight to your inbox.
Subscribe Free