Best links of the week #2

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Best links of the week from 14th January to 20th January.


  1. Data Hackers BR is apparently the largest community for Data Science in Brazil. They own a blog, a podcast, a Slack channel and a mailing list.
  2. Peeriodicals is a platform where you can compile a set of [existing] articles into a virtual journal, becoming its Editor-in-chief. People can subscribe to your jornal and follow your editions, just like they would in a real journal.
  3. Alerta máximo contra as pseudociências at Unicamp Notícias.
  4. TLM is a new open-source project that aims to create an interactive textbook containing A-Z explanations of concepts and methods, algorithms and their code implementations from the fields of data science, machine learning, deep learning, natural language processing, statistics, and more. TLM also seeks to link these fields to each other, highlighting their correlation.
  5. Cookiecutter Data Science: A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
  6. Faster Data Science Education at Kaggle.


  1. Americans want to regulate AI but don’t trust anyone to do it at MIT Technology Review.
  2. A neural network can learn to organize the world it sees into concepts— just like we do at MIT Technology Review.
  3. Software brasileiro transmitirá dados coletados pela maior camera digital do mundo at Inovação Tecnológica.
  4. We all learn from the very beginning of our academic careers that correlation does not imply causation. But does causation imply correlation? Focus on the answer of Carlos Cinelli. Seen at Stack Exchange.
  5. Answers, but mainly Carlos Cinelli’s answer on The Book of Why by Judea Pearl: Why is he bashing statistics? at Stack Exchange.
  6. What They Don’t Teach You in Machine Learning Courses at Taxify Labs.
  7. Rent-scraping: How I saved money using R at Embracing the Random.
  8. A ordem imaginada e o branding sensorial at Nossa Ciência.
  9. Como é a Orientação das Ruas das Capitais Brasileiras at Marcus Nunes’ Blog.
  10. ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus at inFERENCe.
  11. The Blessings of Multiple Causes: Causal Inference when you Can’t Measure Confounders at inFERENCe.

Scientific Articles

  1. Converting Cancer Cells to Fat Cells to Stop Cancer’s Spread at Technology Networks. Paper here at Cancer Cell.
  2. What do opponents of genetically modified foods really know about the science? at Penn Today. Paper here at Nature Human Behaviour.
  3. Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning at Scientific Reports.
  4. Inteligência artificial confirma existência de “nova” espécie de hominídeo at EXAME Tecnologia. Paper here at Nature Communications.
  5. Screen time: how much is too much? at Nature. Paper here at Nature Human Behaviour.
  6. Physiological and Genetic Adaptations to Diving in Sea Nomads at Cell.


  1. Learning Causal Mechanisms by Bernhard Schölkopf at the International Conference on Learning Representations.