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My extensive job searching lately has been really frustrating.No matter how badly you want the job, it’s sometimes difficult to stay focused and submit interesting, correct materials–and even when you do you are hoping the position still exists by the time you apply.New sites like 1st Gi solve so many of these problems.Focused on early career individuals, it creates a match on both the employer and seeker side.

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Everyone seems to be collecting it, analyzing it, making money from it and celebrating (or fearing) its powers.Whether we’re talking about analyzing zillions of Google search queries to predict flu outbreaks, or zillions of phone records to detect signs of terrorist activity, or zillions of airline stats to find the best time to buy plane tickets, big data is on the case. ” champion Watson, has involved the substantial crunching of large bodies of data.By combining the power of modern computing with the plentiful data of the digital era, it promises to solve virtually any problem — crime, public health, the evolution of grammar, the perils of dating — just by crunching the numbers. “In the next two decades,” the journalist Patrick Tucker writes in the latest big data manifesto, “The Naked Future,” “we will be able to predict huge areas of the future with far greater accuracy than ever before in human history, including events long thought to be beyond the realm of human inference.” Statistical correlations have never sounded so good. There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I. The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful.A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply.But it’s hard to imagine there is any causal relationship between the two.

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