NYU Data Science newsletter – February 9, 2016

GROUP CURATION: N/A

I have tried to classify the various ways in which computerization affected the development of economics.

I am thrilled to see that several computer scientists were among the class.

Actually in putting together the list below of computer members I had some difficulty. If I made an error I apologize in advance. One of the interesting results of looking at the whole list of 80 members is that computing played a major role in many member’s citations. Yet for many of these I would not classify them as computer scientists. This is just another example of the importance of computing in all of engineering. So if I left out someone from what I considered another area, well I am sorry.

I love hackathons. I can learn more in a day or two of hard work with friends than I could in six months studying on my own. So when I heard about the first Social Data Science Hackathon in the Twin Cities, I was one of the first to sign up.

The hackathon took place on November 14th in a cavernous conference room at the headquarters of General Mills, just outside Minneapolis. With four teammates, I sweated over data science problems (and munched on General Mills snacks) for eight hours, then presented our work to a panel of esteemed judges. Here are a few of the most important lessons I learned from the experience.

… Twenty years after Barlow declared cyberspace independent, I myself was in Davos for the WEF annual meeting. The Fourth Industrial Revolution was the theme this year, and a big part of me was giddy to go, curious about how such powerful people would grapple with questions introduced by technology.

What I heard left me conflicted and confused. In fact, I have never been made to feel more nervous and uncomfortable by the tech sector than I did at Davos this year.

Python is a premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. With the popularity of Python programming language moving steadily along an upward trajectory, the time is right to master Python in order to land Python jobs. In this bog post, we will discuss various Python career opportunities available for a Python programmer.

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

In the rock-paper-scissors game of technology, the Internet of Things beats cryptography. This is the conclusion of a new Harvard Law School report focusing on the FBI’s claims that increasing levels of cryptography in consumer devices means that law enforcement loses.

The report retorts that even if cryptography closes some doors, new Internet-connected devices and services will open others.

A study by scientists at NASA’s Jet Propulsion Laboratory in Pasadena, California, and Duke University in Durham, North Carolina, shows, in detail, the reason why global temperatures remain stable in the long run unless they are pushed by outside forces, such as increased greenhouse gases due to human impacts.

Lead author Patrick Brown, a doctoral student at Duke’s Nicholas School of the Environment, and his JPL colleagues combined global climate models with satellite measurements of changes in the energy approaching and leaving Earth at the top of the atmosphere over the past 15 years. The satellite data were from the Clouds and the Earth’s Radiant Energy System (CERES) instruments on NASA’s Aqua and Terra spacecraft. Their work reveals in new detail how Earth cools itself back down after a period of natural warming.

In the following post, I will show how to build a scatter plot matrix that is “spiked” with some boxplots to highlight some useful statistics. I will proceed step by step. First I will explain how to prepare the dataset for plotting. Then I’ll explain how to plot a scatterplot and a boxplot using basic plotly syntax. Finally, I’ll show you how to put all the pieces together to build the final plot. If you want to jump straight to the plotting, just skip the Getting to know the dataset and Simplification of the dataset sections and go to Isolated Scatterplot.

From speaking with various analysts, there seems to be very little conversation about ways of working and improving upon them. The reason for this is fairly obvious, analysts are essentially in competition with one another and want to keep a competitive edge over their rivals.

But this doesn’t mean that everything should be kept a secret.

The following list contains tools that have made me a better (read: more productive, skilled and informed) analyst. Hopefully this list serves as a useful resource, and maybe even strike up a conversation with others offering up tools they have found useful too.