Hello and Welcome back!
This series is my attempt to start cataloging all the interesting articles, industry reports, whitepapers, and news that I read every month, related to technology and data science. We are at Month 3 already.
Check out this article in the Financial Times written by the co-founder of Swiftkey. He says
Before we make any conclusions though, just pause for a moment, take a deep breath, and then press play on this video. Also, get ready for the takeover! https://pbs.twimg.com/tweet_video/CJwcPAoWUAAi3s4.mp4
Along the same lines, here was an interesting account of what happened to the Google self-driving car when it came face to face with a bicyclist ...
Following the Ashley Madison fiasco from last month, headline generating stories have sprouted demanding that more controls are placed on the personally identifiable information in big databases so that this elusive and disappearing concept of anonymity can still be maintained.
I am quoting from this article in the Economist from last month –
“People want both perfect privacy and all the benefits of openness. But they cannot have both.”
This reminds me of the plethora of court cases and lawsuits we had a decade ago about the NIMBY (Not In My Back Yard) issue – we wanted cell service at home, but we weren’t willing to loosen zoning laws to allow for cell towers to go up because we considered cell towers ugly. Thanks to NIMBY, I had my master’s thesis cut out for me! Anyway, journeying back into data science, here is a data viz on this issue: http://dadaviz.com/s/ashley-madison-revealed.
This blog post was an excellent 15 min read on the journey of a data scientist at Twitter over the past 2 years.
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it” — Dan Ariely
There are two types of data scientists: the ones that talk with computers and those that talk with humans! That classification is fairly accurate in my opinion, because the skill-set needed for the two are very different. To talk to computers, one must be proficient with software engineering and to talk to humans, one must have excellent story telling capabilities (along with understanding how to design effective graphics)
In this article, Robert Chang talks about a classification of data scientists into Type A (Analysis) and Type B (Building) data scientists which I found to be interesting.
It gets really challenging when you want to buy geeky gifts for your geeky best friends when you don’t know what exactly you are looking for, and you decide to go on Etsy because you are short of time, but end up spending hours wading through thousands of geeky gifts before you find the right one. Don’t fret, because now Etsy refined their classification algorithms recently using principles from thermodynamics (entropy) and topic modeling.
Also note how a simple model can be very effective. I kept that in mind this week, as I was working on a really interesting ranking, tier-ing and classification exercise.
I mentioned Google’s Public Data Explorer in my previous post. The Center for International Development at Harvard Kennedy School of Government has a similar website dedicated to understanding complexities in economics. They host a lot of data and analysis here on the imports and exports from different countries.
That's all for this time. Check back again in a few weeks for more!
Just in case you aren’t satisfied yet: Check out DJ Patil’s A Six Month Update on How We’ve Been Using Data, and How it Benefit....