Teaching Data Science to High Schoolers

Visualizing Dogs’ medical history. Centrality Measures. Guide to Explainability in ML Models. Ursa Labs. Intro to Tensorflow.  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌  [DataCamp](  ISSUE #44 — APRIL 24, 2018  [DataCamp Weekly]( Hi there - some housekeeping today before we get to our Community content. The first item on the list is to announce our upcoming webinar, [Democratizing Data Science Within Your Company](, by DataCamp’s Chief Data Scientist David Robinson. The webinar will take place tomorrow Wednesday 4/25 at 11am ET. We have limited seats, so RSVP by following the link above! The second item is to announce a new podcast episode that launched yesterday: Automated Machine Learning (with Randy Olson). In this episode, Hugo & Randy tackle the subject of automated machine learning and data science, what will that look like and which verticals will be the most disrupted. Check out the episode [here](. Finally, you have another invitation to join us on Thursday at 9:30 am ET on Facebook for a new live-coding session. Hugo will show you how R can make art by giving a walkthrough of a DataCamp Project. All you need a DataCamp profile to follow along. No extensive R experience is required, let us know you’re coming on the [event page!]( And now for this week’s most upvoted articles on the Community. Some highlights of this week’s posts include teaching data science to high schoolers, network analysis in R: centrality measures, announcing Ursa labs and more. Enjoy!  Community Top Posts  [1]( [Teaching Data Science to High Schoolers](  TIDYVERSE  |  Posted by drobinson  [Upvotes]( 17 DataCamp’s Michael Chow shares his experience teaching data science to high school students with both R & Python using data from the Spotify API.  [2]( [A Shiny App to Visualize and Share My Dogs' Medical History](  SHINY  |  Posted by jallen1006  [Upvotes]( 10 A post about the more challenging aspects of creating a shiny app to visualize pets’ medical history. Topics include using databases with shiny, Amazon S3, reactivePoll(), writeBin() and the timevis package.  [3]( [Privacy Pro's Guide to Explainability in Machine Learning Models](  PRIVACY  |  Posted by clovissix  [Upvotes]( 9 With the GDPR's implementation date looming, there has been much discussion about whether the regulation requires a right to an explanation from machine learning models.  [4]( [Network Analysis in R: Centrality Measures](  R PROGRAMMING  |  Posted by MinooAshtiani  [Upvotes]( 9 Explore the definition of centrality, learn what different types of centrality measures exist in network analysis and pick the best one for a given network.  [5]( [Announcing Ursa Labs: an innovation lab for open source data science](  OPEN DATA  |  Posted by hugobowne  [Upvotes]( 7 Wes McKinney (Pandas) announces Ursa Labs, an innovation lab for open source data science, in collaboration with Hadley Wickham (dplyr, ggplot2) among others.  [6]( [Deep Learning at NVIDIA (with Michelle Gill)](  PODCAST  |  Posted by hugobowne  [Upvotes]( 6 DataFramed episode on the modern superpower of deep learning and where it has the largest impact, past, present, and future, filtered through the lens of Michelle Gill's work at NVIDIA.  [7]( [How to Manage Title Confusion in Data Science](  BUSINESS  |  Posted by lindaburtch  [Upvotes]( 6 As anyone who has tried to discern the true definition of a data scientist knows, titles can vary. How can you manage role expectations while job searching and know what you're getting into?  [8]( [Introduction to TensorFlow](  DEEP LEARNING  |  Posted by shubhamsharma1318  [Upvotes]( 6 TensorFlow is a framework which is used for machine learning and deep learning applications like neural networks. This post is an introduction to this popular framework.  [9]( [Hierarchical Clustering on Categorical Data in R](  MACHINE LEARNING  |  Posted by anastasiareusova  [Upvotes]( 6 This blog post covers the basics of hierarchical clustering when performed on categorical data with code samples.  [10]( [Piping in R](  R PROGRAMMING  |  Posted by statworx  [Upvotes]( 5 You may have worked with %>% and felt that you have reached the limit. Rest assured, everything is pipeable! To understand this, you need to know the following: every operator in R is a function.   [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