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.
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?
January 15, 2010 — Jon McLoone, International Business & Strategic Development
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
December 17, 2009 — Wolfram Blog Team
Using Mathematica‘s powerful data handling and data visualization capabilities, Drouillard is gaining a deeper understanding and more accurate picture of how clients are using BondDesk’s platform to search for fixed income securities. In this video, he describes how Mathematica helps him go deeper inside the interface, resulting in richer insights at a more efficient rate than ever before.
May 8, 2009 — Kelvin Mischo, Sales Engineer
With all the new aspects of Mathematica in Versions 6 and 7, I’ve enjoyed visiting universities to talk about how to use Mathematica in even more courses and research projects. Universities enjoy this, too!
I am not, however, very good at thinking about the locations of universities or schools in terms of geography. Planning a trip was a seemingly endless task of cross-referencing maps and lists and notes and more lists—I’m sure you see a pattern forming here.
The solution, as is often the case with me, was to use Mathematica. After finding a list of 7,000+ universities and colleges in the United States, I wrote a Mathematica program to create a list of all such schools near a particular city, complete with rough mileage and a map to use for my work.
February 3, 2009 — Faisal Whelpley, User Interface Group
Consider the typical infographics found on the internet, many of which are only slightly less silly than this one by Jamie Schimley:
If you want to regenerate a chart such as this in Mathematica using the PieChart function, you need hard data: the relative areas of the slices. You could eyeball the values and get an approximation, but since I deal with user interfaces I was immediately interested in creating one that would allow me to measure the angle of each sector of a pie chart.
The following code creates locators that can be positioned to calculate the angle of any sector. Buttons let you record the angles as you measure them, and reproduce the chart at the end. (This could be done with less code, but I wanted a more complete interface with finishing touches like disabling the Print Chart button if you haven’t measured any angles yet, and showing the current angle with a tooltip.)
Weather visualizations are very interesting—there are television channels that thrive by showing nothing else. Online, there are several sources for specific maps of current weather conditions. Generally these are produced and maintained by government agencies or other large organizations. But with Mathematica 7, you can easily produce completely customizable weather visualizations on your own computer.
As usual, this is made possible by Mathematica‘s tight integration of several areas of functionality. Two new features that enable this particular application are powerful new vector visualization functions and built-in weather data.
Vector visualization has been present in Mathematica since Version 2. In Mathematica 7 it has been dramatically improved, adding modern techniques in vector data visualization and new algorithms developed at Wolfram Research. Traditional arrow-based vector plots, new methods based on automatic streamline placement, support for vector glyphs, and high-resolution images produced using line integral convolutions are all now supported.