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.

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?

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.

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.

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?

Like most in the United Kingdom, I have been trapped in my house by snow for most of the last week. Waking up again like Bill Murray in Groundhog Day, to another snowy view, I have been dreaming of summer days to come. It was against this background that I thought I would get around to testing whether an old British weather proverb was true: St. Swithun's day if thou dost rain For forty days it will remain St. Swithun's day if thou be fair For forty days 'twill rain no more