Reblog: Fun Articles

These are a collection of articles I’ve read over the past couple months. I think they’re pretty interesting, so I thought I’d reblog them.

“The Rise of Stephen Curry” (Grantland): Focuses in particular on Curry’s college career, which is–needless to say–impressive. Fun fact: Curry “was so electric that his worst-ever statistical performance [in college] also became one of the most bizarre college basketball games ever played, as Loyola defended him so single-mindedly that it held him scoreless — but still lost to Davidson [Curry’s college] by 30.” You know he’s good when a team spends their entire game making sure he doesn’t score a point, and doesn’t care about losing by 30. The author also suggests that there’s a certain joy to watching Curry play, which I agree with. (You know, out of the 1.25 Warriors games I’ve seen.)

“Stephen Curry: The Full Circle” (ESPN): This article talks a bit about Curry’s childhood and how he trained as a kid, which I found intriguing. He learned basketball in the most difficult circumstances, setting him up to do well later on when he wasn’t playing outdoors on a (very) non-regulation hoop.

“How the CIA Used a Fake Sci-Fi Flick to Rescue Americans From Tehran” (Wired): The backstory for the movie “Argo,” which I watched recently. (A good movie! In the heist genre.) During the Iran Hostage Crisis in 1979-1981, several US State Department employees were hiding in Iran, trying not to get captured. CIA officer Tony Mendez used his subterfuge skills to extract the six employees. Their cover: a sci-fi film crew. Good read and a good accompanying movie–“Argo” won the Best Picture award at the Oscars. (If you thought this was interesting, see “How a Brilliant Intelligence Officer Used ‘Monopoly’ to Free WWII POWs,” HT Challies.)

“The Future of Data Visualization” (Jeffrey Heer, Strata + Hadoop 2015): OK, not an article; it’s a video. But I’m including it because I’m a statistics major. It’s pretty short–10 minutes long–but talks about how helpful data visualizations can be at discovering patterns in the data. There’s also a really helpful graphic at 3:00 ranking different kinds of ways to compare quantities, from most helpful to least helpful. If you want insight into what makes a helpful visualization, watch this video.


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