"data analysis" entries

Now available: Big Data Now 2012 Edition

O'Reilly's annual data anthology explores the maturation of big data and data science.

Big Data Now 2012 EditionIn the first edition of our free Big Data Now anthology, the O’Reilly team tracked the birth and early development of data tools and data science. Now, with the second edition, we’re seeing what happens when big data grows up: how it’s being applied, where it’s playing a role, and the consequences — good and bad alike — of data’s ascendance.

We’ve organized the 2012 edition of Big Data Now into five areas:

Getting Up to Speed With Big Data — Essential information on the structures and definitions of big data.

Big Data Tools, Techniques, and Strategies — Expert guidance for turning big data theories into big data products.

The Application of Big Data — Examples of big data in action, including a look at the downside of data.

What to Watch for in Big Data — Thoughts on how big data will evolve and the role it will play across industries and domains.

Big Data and Health Care — A special section exploring the possibilities that arise when data and health care come together.

You can download free editions of Big Data Now 2012 in PDF, Mobi and EPUB formats here. The 2011 edition is also available.

Deconstructing a Twitter spam attack

Data analysis shows the structure of a network can separate true influencers from fake accounts.

There has been a lot of discussion recently about the effect fake Twitter accounts have on brands trying to keep track of social media engagement. A recent tweet spam attack offers an instructive example.

On the morning of October 1, the delegates attending the Strata Conference in London started to notice that a considerable number of spam tweets were being sent using the #strataconf hashtag. Using a tool developed by Bloom Agency, with data from DataSift, an analysis has been done that sheds light on the spam attack directed at the conference.

The following diagram shows a snapshot of the Twitter conversation after a few tweets had been received containing the #strataconf hashtag. Each red or blue line represents a connection between two Twitter accounts and shows how information flowed as a result of the tweet being sent. By 11 a.m., individual communities had started to emerge that were talking to each other about the conference, and these can clearly be seen in the diagram.

Strataconf tweeting communities

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MATLAB, R, and Julia: Languages for data analysis

Inside core features of specialized data analysis languages.

Big data frameworks like Hadoop have received a lot of attention recently, and with good reason: when you have terabytes of data to work with — and these days, who doesn’t? — it’s amazing to have affordable, reliable and ubiquitous tools that allow you to spread a computation over tens or hundreds of CPUs on commodity hardware. The dirty truth is, though, that many analysts and scientists spend as much time or more working with mere megabytes or gigabytes of data: a small sample pulled from a larger set, or the aggregated results of a Hadoop job, or just a dataset that isn’t all that big (like, say, all of Wikipedia, which can be squeezed into a few gigs without too much trouble).

At this scale, you don’t need a fancy distributed framework. You can just load the data into memory and explore it interactively in your favorite scripting language. Or, maybe, a different scripting language: data analysis is one of the few domains where special-purpose languages are very commonly used. Although in many respects these are similar to other dynamic languages like Ruby or Javascript, these languages have syntax and built-in data structures that make common data analysis tasks both faster and more concise. This article will briefly cover some of these core features for two languages that have been popular for decades — MATLAB and R — and another, Julia, that was just announced this year.


MATLAB is one of the oldest programming languages designed specifically for data analysis, and it is still extremely popular today. MATLAB was conceived in the late ’70s as a simple scripting language wrapped around the FORTRAN libraries LINPACK and EISPACK, which at the time were the best way to efficiently work with large matrices of data — as they arguably still are, through their successor LAPACK. These libraries, and thus MATLAB, were solely concerned with one data type: the matrix, a two-dimensional array of numbers.

This may seem very limiting, but in fact, a very wide range of scientific and data-analysis problems can be represented as matrix problems, and often very efficiently. Image processing, for example, is an obvious fit for the 2D data structure; less obvious, perhaps, is that a directed graph (like Twitter’s follow graph, or the graph of all links on the web) can be expressed as an adjacency matrix, and that graph algorithms like Google’s PageRank can be easily implemented as a series of additions and multiplications of these matrices. Similarly, the winning entry to the Netflix Prize recommendation challenge relied, in part, on a matrix representation of everyone’s movie ratings (you can imagine every row representing a Netflix user, every column a movie, and every entry in the matrix a rating), and in particular on an operation called Singular Value Decomposition, one of those original LINPACK matrix routines that MATLAB was designed to make easy to use.

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Statwing simplifies data analysis

Quickly perform and interpret the results of routine Small Data analysis

With so much focus on Big Data, the needs of many analysts who work with Small Data tend to get ignored. The default tool for many of these users remains spreadsheets1 and/or statistical packages which come with a lot of features and options. However many analysts need a very small subset of what these tools have to offer.

Enter Statwing, a software-as-a-service provider for routine statistical analysis. While the tool is still in the early stages, it can already do many basic “data analysis” tasks.

Consider the following example of a pivot table constructed in Excel: this required 8 mouse-clicks, if you do everything perfectly, and about 5 decisions (what variables to include, what metric to use, …)

The same task in Statwing required 4 mouse-clicks and 0 decisions! Plus it comes with visuals:

The lack of clutter and the addition of a simple “headline” (“Female tends to have much higher values for satisfaction than Male“), makes the result much easier to interpret. The advanced tab contains detailed statistical analysis (in this case the p-value, counts, values). Many users get confused by the output/results produced by traditional statistical software. Let’s face it, many analysts have had little training in statistics. I welcome a tool that produces readily interpretable results.

The company hopes to replicate the above example across a wide variety of routine data analysis tasks. Their initial focus is on tools for (consumer) survey analysis, a potentially huge market given that online companies have made surveys so much easier to conduct. Users of Statwing pay a small monthly subscription, making it cheaper than most2 statistical packages. For a small monthly fee, their intuitive UI lets analysts get their tasks done quickly. More importantly Statwing may nurture aspiring data scientists in your organization.

(1) As this recent Strata presentation points out: Spreadsheets are the glue that keeps many organizations together.

(2) Open source tools like OpenOffice, R and Octave are free. So is the use of Google spreadsheets.

Stories over spreadsheets

Kris Hammond on replacing rows and columns with sentences and paragraphs.

Imagine a future where clear language supplants spreadsheets. In a recent interview, Narrative Science CTO Kris Hammond explained how we might get there.

Operations, machine learning and premature babies

An astonishing connection between web ops and medical care.

Machine learning and access to huge amounts of data allowed IBM to make an important discovery about premature infants. If web operations teams could capture everything — network data, environmental data, I/O subsystem data, etc. — what would they find out?

Data as seeds of content

A look at lesser-known ways to extract insight from data.

Visualizations are one way to make sense of data, but they aren't the only way. Robbie Allen reveals six additional outputs that help users derive meaningful insights from data.

Automated science, deep data and the paradox of information

Be aware of the just-so data stories that sound reasonable but cannot be conclusively proven.

Bradley Voytek: "Our goal as data scientists should be to distill the essence of the data into something that tells as true a story as possible while being as simple as possible to understand."

The unreasonable necessity of subject experts

Experts make the leap from correct results to understood results.

We can't forget that data is ultimately about insight, and insight is inextricably tied to the
stories we build from the data. Subject experts are the ones who find the stories data wants to tell.

Profile of the Data Journalist: The Homicide Watch

Chris Amico and Laura Norton Amico's project started as a spreadsheet. Now it's a community news platform.

To learn more about the people who are redefining the practice computer-assisted reporting, in some cases, building the newsroom stack for the 21st century, Radar conducted a series of email interviews with data journalists during the 2012 NICAR Conference. "It’s not just about the data, and it’s not just about the journalism, but it’s about meeting a community need in an innovative way," said Laura Norton Amico.