November 14, 2012 — Jon McLoone, International Business & Strategic Development

I stumbled upon a nice project called Rosetta Code. Their stated aim is “to present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another.”

After amusing myself by contributing a few solutions (Flood filling, Mean angle, and Sum digits of an integer being some of mine), I realized that the data hidden in the site provided an opportunity to quantify a claim that I have often made over the years—that *Mathematica* code tends to be shorter than equivalent code in other languages. This is due to both its high-level nature and built-in computational knowledge.

Here is what I found.

*Mathematica* code is typically less than a third of the length of the same tasks written in other languages, and often much better.

October 24, 2012 — Jason Martinez, Research Programmer

Earlier this month, on a nice day, Felix Baumgartner jumped from 39,045 meters, or 24.26 miles, above the Earth from a capsule lifted by a 334-foot-tall helium filled balloon (twice the height of Nelson’s column and 2.5 times the diameter of the Hindenberg). Wolfram|Alpha tells us the jump was equivalent to a fall from 4.4 Mount Everests stacked on top of each other, or falling 93% of the length of a marathon.

At 24.26 miles above the Earth, the atmosphere is very thin and cold, only about -14 degrees Fahrenheit on average. The temperature, unlike air pressure, does not change linearly with altitude at such heights. As Wolfram|Alpha shows us, it rises and falls depending on factors such as the decreased density of air with rising altitude, but also the absorption of UV light by the ozone layer.

At 39 kilometers, the horizon is roughly 439 miles away. At this layer of the atmosphere, called the stratosphere, the air pressure is only 3.3 millibars, equivalent to 0.33% of the air pressure at sea level. To put it another way, the mass of the air above 39 kilometers is only 0.32851% of the total air mass. Given this knowledge, we know that 99.67% of the world’s atmosphere lay beneath him. This information was important to Felix’s goal to break the sound barrier in free fall because the rate of drag is directly related to air pressure. With less air around him, there would be less drag, and thus he could reach a higher maximum speed. Of course, this would require him to wear an oxygenated suit to allow him to breathe, in addition to keeping him warm.

August 29, 2012 — William Sehorn, Software Technology

In Stephen Wolfram’s personal analytics blog post, he showed a number of interesting plots of the steps he’s recorded on his pedometer over the past two years. Each plot highlighted a different feature of his activity. For example, this daily step distribution makes it clear that Stephen is typically most physically active around noon:

In this blog post I’ll show you how to analyze your own pedometer data and make cool plots like Stephen’s. If you don’t have any data, you can use the attached sample data that corresponds to my own physical activity.

First we need to import the data and format it appropriately.

The data is formatted as pairs of time stamps and step counts in five-minute intervals.

June 14, 2012 — Paul-Jean Letourneau, Senior Data Scientist, Wolfram Research

Wouldn’t it be cool if you never had to remember another password again?

I read an article in *The New York Times* recently about using individual typing styles to identify people. A computer could authenticate you based on how you type your user name without ever requiring you to type a password.

To continue our series of posts about personal analytics, I want to show you how you can do a detailed analysis of your own typing style just by using *Mathematica*!

Here’s a fun little application that analyzes the way you type the word “wolfram.” It’s an embedded Computable Document Format (CDF) file, so you can try it out right here in your browser. Type “wolfram” into the input field and click the “save” button (or just press “Enter” on your keyboard). A bunch of charts will appear showing the time interval between each successive pair of characters you typed: w–o, o–l, l–f, f–r, r–a, and a–m. Do several trials: type “wolfram,” click “save,” rinse, and repeat (if you make a typo, that trial will just be ignored).

May 1, 2012 — Carlo Barbieri, Applied Research Group

In spring 2011, while adding the finishing touches to my PhD dissertation, I decided to enroll in the Wolfram Science Summer School (then called the NKS Summer School). I never suspected that my project at the Summer School would lead to a job and my involvement in one of the central features of Wolfram|Alpha Pro.

During my years as a graduate student I had the chance to live in three different countries and experience different working environments: other than my native Italy, I lived in Paris, where my PhD was based at ENS, and in Princeton, where I was lucky enough to spend time at the Institute for Advanced Study. However, at the end of my PhD, I felt that most of my interest in what I was doing was gone and that I needed to try something new.

Once at the Summer School, I had the chance to meet and chat with Stephen Wolfram as he helped me come up with a problem to work on. One of the first things I told him was that I was weary of open-ended academic kinds of problems and I was afraid no one was ever going to read my papers. I said that I wanted to deal with intellectual challenges, but I also wanted to tackle something that had a clear beginning and end.

His reply came as a disappointment, since what he suggested I work on was both completely outside my area of expertise and clearly one of those impossibly wide problems that I was now skeptical of. What did he say?

April 5, 2012 — Paul-Jean Letourneau, Senior Data Scientist, Wolfram Research

In Stephen Wolfram’s recent blog post about personal analytics, he showed a number of plots generated by analyzing his archive of personal data. One of the most common pieces of feedback we received was that people wanted to know how they could perform the same kind of analysis on their own data. So in this blog post I’m going to show you how to analyze your email the same way Stephen Wolfram did.

Naturally, we did all the data cleaning and analysis for Stephen’s data in *Mathematica*, so we’ll be using *Mathematica* for everything here as well. All the code can be downloaded here.

Let’s start with that really cool diurnal plot Stephen did of his outgoing email. This plot shows the date and time each email was sent, with years running along the *x* axis and times of day on the *y* axis:

February 9, 2012 — Stephen Wolfram

It’s a sad but true fact that most data that’s generated or collected—even with considerable effort—never gets any kind of serious analysis. But in a sense that’s not surprising. Because doing data science has always been hard. And even expert data scientists usually have to spend lots of time wrangling code and data to do any particular analysis.

I myself have been using computers to work with data for more than a third of a century. And over that time my tools and methods have gradually evolved. But this week—with the release of Wolfram|Alpha Pro—something dramatic has happened, that will forever change the way I approach data.

The key idea is automation. The concept in Wolfram|Alpha Pro is that I should just be able to take my data in whatever raw form it arrives, and throw it into Wolfram|Alpha Pro. And then Wolfram|Alpha Pro should automatically do a whole bunch of analysis, and then give me a well-organized report about my data. And if my data isn’t too large, this should all happen in a few seconds.

And what’s amazing to me is that it actually works. I’ve got all kinds of data lying around: measurements, business reports, personal analytics, whatever. And I’ve been feeding it into Wolfram|Alpha Pro. And Wolfram|Alpha Pro has been showing me visualizations and coming up with analyses that tell me all kinds of useful things about the data.

July 15, 2010 — Deepa Nair, Technical Communications & Strategy

In recent years, predicting the health of the U.S. economy has become more complicated than ever. Economists are constantly on the lookout for new ways to predict the economy’s future path, but discovering significant new economic indicators has become more difficult.

The Kronos Retail Labor Index is an exciting new leading economic indicator of the overall health of the U.S. economy. Dr. Robert Yerex, chief economist at Kronos, used *Mathematica* exclusively in its development and monthly production.

May 19, 2010 — Wolfram Blog Team

Wolfram Research hosts lots of popular websites, including Wolfram|Alpha and the Wolfram Demonstrations Project, and we collect a lot of web traffic data on those sites to make sure you, our visitors, are meeting your goals. To really dive deep into that data, our corporate analysis team has built on a number of *Mathematica*‘s standard data analysis features to develop a powerful, in-house computable data function for studying web traffic and other business data.

In this video, corporate analysis team lead David Howell describes how using *Mathematica* gives his team huge advantages in discovering new patterns and relationships within our web traffic data and in delivering insightful interactive reports.

May 3, 2010 — Jon McLoone, International Business & Strategic Development

As the closing days of the United Kingdom election campaign have focused on the economy, I thought I would repeat the analysis that Theodore Gray did on Dow Jones returns under United States presidential parties—but using UK data.

I started by going to an interactive *Mathematica* Demonstration that Theodore wrote. Like all Demonstrations, it doesn’t just present information, it encodes the analysis, so by downloading the source code, I was able to re-deploy it on UK data quite quickly. The data was a little more difficult (detailed at the end of this post).

So what did I find?