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Can data ever be significant without interpretation? In a recent Atlantic article on the rise of the data artist, a stark distinction is drawn between data presentations and data art. Visualization projects like R. Luke Dubois' "A More Perfect Union" and David McCandless' various "Information is Beautiful" projects fall on the side of the latter, whereas projects such as "Rich Blocks Poor Blocks" might be considered "closer to the data". However what are the implications of seeing certain approaches to data analytics as artistic and others as more scientifically objective? The presence of the artist makes itself known in projects that explicitly use visuals to evoke sentiments in the individual, who surrounded in a modern sea of information, must grapple with the ways in which their identities and environments are being newly defined. But the process of cleaning data, selecting variables, and asking certain questions of a dataset could be defined as an artistic process as well, imbued with individual choices and altered by ideological conditioning.

When we look at the total individual contributions to political committees by state, it is apparent that California, New York, and Texas take the lead. Given the fact that these states have the highest population, can we justify a claim that the residents are more generous when it comes to political contributions?

Truth be told, the industry does not have an agreed upon definition of a data scientist. Jokes such as “a data scientist is a data analyst living in the Silicon Valley” are abundant. Below is one such cartoon, just for fun.

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53% of big data-focused companies say analytics experts will be tough to find for the next two years. Here’s how IT leaders plan to train, borrow, or steal talent–and what job seekers should know.

Chief data officers are rising to the leadership of business analytics programmes in the UK and US more quickly than expected, according to an Accenture study that surveyed the field for the first time since 2009.

Reposted from Cloudera blog.

I started using R about 3 years ago. It was slow going at first. R had tricky and less intuitive syntax than languages I was used to, and it took a while to get accustomed to the nuances. It wasn’t immediately clear to me that the power of the language was bound up with the community and the diverse packages available.

So there’s this startup called SmogFarm, which does big-data sentiment analysis, “pulse of the planet” stuff. I spotted them last year, and now they’ve got an actual product with an actual business model up and running in private beta:KredStreet, “The Social Stock Trader Rankings,” which performs sentiment analysis on StockTwits data and a sampling of the Twitter firehose to determine traders’ overall bullish/bearish feeling. They also compare reality against past sentiment to score and rank traders based on their accuracy, which is more interesting.