The Fight Against Cancer

The Fight Against Cancer. Markov Chains. Efficient Experiments. Visualizing Metro Systems.  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌  [DataCamp](  ISSUE #46 — MAY 08, 2018  Hi everyone, and happy Tuesday! Before we jump into this week's data science news, we wanted to share the latest episode from DataFramed: The Fight Against Cancer (with Sandy Griffith). How can data science help in the fight against cancer? What are its limitations? Find out in this conversation from the frontier of research. Happy learning! [Listen Now](  Community Top Posts  [1]( [Markov Chains in Python: Beginner Tutorial](  PYTHON  |  Posted by cjsejal  [Upvotes]( 16 Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python!  [2]( [How efficient are multifactorial experiments?](  STATISTICS  |  Posted by keith-goldfeld  [Upvotes]( 4 I recently described why we might want to conduct a multi-factorial experiment, and I alluded to the fact that this approach can be quite efficient. It is efficient in the sense that it is possible to test simultaneously the impact of multiple interventions using an overall sample size that would...  [3]( [Kaggle and the Future of Data Science (with Anthony Goldbloom)](  DATAFRAMED  |  Posted by hugobowne  [Upvotes]( 3 Anthony Goldbloom, CEO of Kaggle, speaks with Hugo about Kaggle, data science communities, reproducible data science, machine learning competitions and the future of data science in the cloud.  [4]( [rOpenSci unconf18 prep and tidyverse-like (pipe-friendly) functions](  UNCONF18  |  Posted by lcolladotor  [Upvotes]( 3 Getting ready to attend rOpenSci unconf18 and probably working on tidyverse-like functions for the first time  [5]( [Metro Systems Over Time](  VISUALIZATION  |  Posted by pagepiccinini  [Upvotes]( 3 Metro systems are an interesting way to learn more about the growth of a city over time. You can see things like how the city expanded as public transit spread farther and farther from the original city limits. This post examines that growth in R using ggmap and gganimate.  [6]( [2017 SAS, R, or Python Flash Survey Results](  SURVEY  |  Posted by lindaburtch  [Upvotes]( 3 Last year, we asked data science and analytics professionals whether they prefer SAS, R, or Python, and examined their responses by years' experience, industry, education, and trends over the past four years.  [7]( [[ Back to Basics ] Deriving Back Propagation on simple RNN/LSTM (feat.](  MACHINE LEARNING  |  Posted by jaedukseo  [Upvotes]( 3 [ Back to Basics ] Deriving Back Propagation on simple RNN/LSTM (feat. Aidan Gomez)  [8]( [Make RStudio Look the Way You Want — Because Beauty Matters](  RSTUDIO  |  Posted by statworx  [Upvotes]( 3 RStudio is a powerful IDE that helped me so many times but there is one thing that bugs me about it: there is no easy option or interface in RStudio that lets you customize your theme. That being said, I did some research on how to customize its themes by hand with a little hack! Here you go!  [9]( [machine learning](  MACHINE LEARNING  |  Posted by mariaellames  [Upvotes]( 3 This small tutorial is meant to introduce you to the basics of machine learning in R: it will show you how to use R to work with KNN.   [DataCamp Community]( BY THE COMMUNITY, FOR THE COMMUNITY [Discover](  That's all for now. Have a great week!   [DataCamp](  DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118  [Facebook]( [Twitter]( [LinkedIn]( [Instagram]( [YouTube](  [Download on the App Store]( [Get it on Google Play](  [Unsubscribe]( Â