{"id":640823,"date":"2013-01-31T12:00:54","date_gmt":"2013-01-31T17:00:54","guid":{"rendered":"http:\/\/gigaom.com\/?p=599476"},"modified":"2013-01-31T12:00:54","modified_gmt":"2013-01-31T17:00:54","slug":"data-for-dummies-6-data-analysis-tools-anyone-can-use","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/640823","title":{"rendered":"Data for dummies: 6 data-analysis tools anyone can use"},"content":{"rendered":"

If you care only about the cutting edge of machine learning and how to manage petabytes of big data, you might want to quit reading now and just come to our Structure:Data conference<\/a> in March. But if you\u2019re a normal person dealing with mere normal data, you\u2019ll probably want to stick around. Although your data might not be that big or complex, that doesn\u2019t mean it isn\u2019t worth looking at in a new light.<\/p>\n

With that in mind, here are six of the best free tools I\u2019ve come across for helping we mere mortals analyze our data without having to know too much about, well, anything (I\u2019d keep an eye on\u00a0the still-under-wraps Datahero<\/a>, too). I\u2019ve gathered some personal data and tracked down some interesting public data sets to help demonstrate what a novice can do with them. Someone with more skills can certainly do a lot more, and larger datasets will provide greater statistical significance.<\/p>\n

BigML<\/h2>\n

BigML<\/a> is to machine learning what Blue Moon<\/a> is to Belgian ales: a simple approach to something generally more complex \u2014 but also rather accessible and good enough to do the job in a pinch. I explained the service more thoroughly in recent post about it being used to generate predictions of Kickstarter success<\/a>, but here\u2019s how it works, in a nutshell: Users upload and format data (which is actually pretty easy), BigML discovers the myriad relationships between the variables and creates a predictive model, and users enter hypothetical data and receive a prediction.<\/p>\n

I\u2019m pretty bad when it comes to entering my data into Fitbit (see disclosure)<\/em>, but I was relatively<\/em> good for a month this summer as I prepped for the Warrior Dash<\/a>, and that\u2019s the data I used to demonstrate BigML. This prediction of how many calories I can expect to burn in a day would work a lot better if I had a bigger sample size and hadn\u2019t occasionally forgotten to log calories and hours slept, but you get the point. The first image is the model the service generated; the second is the prediction interface.<\/p>\n

\"cals<\/a>\"predict\"<\/a><\/p>\n

Google Fusion Tables<\/h2>\n

The user interface for Google Fusion Tables<\/a>\u00a0 isn\u2019t what I\u2019d call pretty (\u201csparse\u201d is probably a better description), but the still-in-experimental-mode visualization tool sure is easy if your data is nicely formatted. I created this interactive map simply by uploading a publicly available dataset about gun violence<\/a> and clicking the button to create a map:<\/p>\n

\"fusion\"<\/a><\/p>\n

For this simple comparison of gun ownership and gun homicide rates, I just checked the countries by which I wanted to filter the chart. Easy.:<\/p>\n

\"gunscomp\"<\/a><\/p>\n

Infogram<\/h2>\n

If you have really simple data \u2014 like a few columns and a handful of rows \u2014 Infogram<\/a> might be the easiest to use of the bunch. The company launched last year with a variety of infographic templates<\/a>, but it has since expanded to include a large number of charts and graphs, too (including line, pie, pictorial, treemap and bubble). Furthermore, it gives sample data, which you can use as an example to enter your own or format the table you want to upload, and the interactive charts embed nicely into web pages (ours, at least).<\/p>\n

Here are the top 10 things I ate during the time I was logging food via Fitbit, excluding copious amounts of beer, water, coffee and Diet Pepsi that I didn\u2019t record.<\/p>\n

<\/form>\n