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Weekly Selection – Sep 7, 2018

Practical Advice for Data Science Writing

By William Koehrsen – 10 min read

Writing is something that everyone wants to do more of, yet we often find it difficult to get started. We know that writing about our data science projects improves our communication abilities, opens doors, and makes us better data scientists, but we often struggle with thoughts that our writing isn’t good enough or that we don’t have the necessary background / education.


Recurrent Neural Networks: The Powerhouse of Language Modeling

By James Le – 12 min read

During the spring semester of my junior year in college, I had the opportunity to study abroad in Copenhagen, Denmark. I had never been to Europe before that, so I was incredibly excited to immerse into a new culture, meet new people, travel to new places, and, most important, encounter a new language.


Probability concepts explained: Rules of probability

By Jonny Brooks-Bartlett – 5 min read

When I wrote the introductory post to this series, I covered some fundamental probability concepts (marginal, conditional and joint probabilities, independence and mutual exclusivity, and the "and" and "or" rules for combining probabilities).


Deep learning and Soil Science (Part 1, Part 2)

By José Padarian – 7 min read

This is the first article of a Series that I am devoting to the use of Deep Learning in Soil Science. My motivation is to show that Deep learning is useful for things other than to classify photos of cats and dogs, or sentiments. Not that there’s anything wrong with cat and dogs, but there are million of examples of that already…


How to Create Animated Graphs in Python

By Viviane – 5 min read

Matplotlib and Seaborn are some nice libraries in Python to create great looking plots. But these plots are all static and it’s hard to depict the change of data values in a dynamic and pleasingly looking way.


Storytelling for Data Scientists

By Jan Zawadzki – 6 min read

Harvard Business Review recently published an article on what leading Data Scientists actually do. Surprisingly enough, the key skill is communication, rather than experience with statistical models.


How machines understand our language: an introduction to Natural Language Processing

By Emma Grimaldi – 5 min read

Natural Language Processing is for me one of the most captivating fields of data science. The fact that a machine can understand the content of a text with a certain accuracy is just fascinating, and sometimes scary.


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