add a few more data and CI resources

mattmakai · mattmakai · commit 47bd2237b9c3 · 2019-10-29T06:13:53.000-04:00
diff --git a/content/pages/02-development-environments/06-jupyter-notebook.markdown b/content/pages/02-development-environments/06-jupyter-notebook.markdown
@@ -192,3 +192,8 @@ like advanced interactive visualizations.
 * The number of open source Jupyter Notebooks on GitHub is exploding and
   [this post attempts to estimate the growth](https://kyso.io/KyleOS/nbestimate)
   using Python, [pandas](/pandas.html) and a scraped data set.
+
+* [Reproducible Jupyter Notebooks with Docker](https://blog.reviewnb.com/reproducible-notebooks/)
+  explains when to use [Docker](/docker.html) in combination with Jupyter
+  Notebooks as well as the instructions for creating a dockerfile to build
+  your images.
diff --git a/content/pages/03-data/00-data.markdown b/content/pages/03-data/00-data.markdown
@@ -72,12 +72,22 @@ following resources do a great job of telling stories with data. There
 are more links to stories listed on the [data analysis](/data-analysis.html) 
 and [data visualization](/data-visualization.html) pages.
 
+* [Data — from objects to assets](https://www.nature.com/articles/d41586-019-03062-w)
+  covers the history of data collection and usage, from 150 years ago to
+  today. The article covers how initial steps by individual scientists
+  sponsored by wealthy patrons in the 1800s gave way to systematic collection
+  by governments and businesses in the 20th century. A significant amount
+  of personal data is now held by a few dozen large corporations worldwide
+  such as Google, Amazon and Facebook. The article covers some of the
+  implications of data as a valuable asset and in general is a great read
+  as a high-level overview of on this topic.
+
 * [Metadata Investigation : Inside Hacking Team](https://labs.rs/en/metadata/)
   presents what metadata is and how it can be used to track people even though
   it is often thought of as less of a problem than typical stored data.
 
 * [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
-  provide a spectacular example of 
+  is a spectacular example of 
   [data visualization](/data-visualization.html) to explain what a machine
   learning model does on a San Francisco and New York housing data set.
 
diff --git a/content/pages/05-deployment/27-continuous-integration.markdown b/content/pages/05-deployment/27-continuous-integration.markdown
@@ -81,6 +81,10 @@ the application was written in.
   practices for the build and test & release cycles in mind. 
   [Go CD source code is on GitHub](https://github.com/gocd/gocd).
 
+* [Bazel](https://bazel.build/) is a build tool that works with CI tools
+  to organize large code bases and provide consistency with a well-defined,
+  automated build process.
+
 * [BuildBot](http://buildbot.net/) is a continuous integration **framework** 
   with a set of components for creating your own CI server. It's written in
   Python and intended for development teams that want more control over
