Google made some big improvements to its Google Trends product this week. There's now extensive real-time data, curated datasets, and a new homepage that's all about data stories. This is definitely worth exploring and be sure to watch the video at the bottom if you're interested in where this is going.
This post in Google's research blog explores some simple techniques for looking inside neural networks. The results are fascinating and have generated a lot of discussion around the web this week. The article is easy to follow and it's also worth checking out the discussion in Hacker News where it had over 700 upvotes within a day of being posted.
Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. If you use R, definitely check out this site by Hadley Wickham that goes along with his new book R Packages. The site includes the complete text and downloadable code.
Nice collection of curated iPython notebooks for Data Science. These notebooks cover topics for Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
Online text for a computational statistics course at Duke University. The text is Python-based but there are lots of code samples and it should be easy to follow even if you're not a Python pro. This is definitely worth bookmarking.
And to make sure that your data stories are presented accurately, make sure you're aware of the visual tricks in this article. Some of these would be easy to stumble into and could result in unintended spin.
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