What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis

“创造性破坏”是奥地利经济学家约瑟夫·熊彼特最著名的理论，当一个产业在革新之时，都需要大规模地淘汰旧的技术与生产体系，并建立起新的生产体系。电器之于火器、汽车之于马车、个人计算机之于照排系统，都是一次又一次的“创造性破坏”，旧的体系完全不复存在，新的体系随之取代。 “创造性破坏”已经深深地改变了我们的生活，在这个数字时代，我们身边的一切都被“数字化”了。只有一处，也许是由于其本身的根深蒂固，也许是由于它天然的排斥新鲜事物，医学，却从未被数字化浪潮所影响。 这本书所谈的，就是一个即将被“创造性破坏”的产业，一个即将被颠覆的产业。iPhone、云计算、3D打印、基因测序、无线传感器、超级计算机，这些改变了我们生活的事物，将再一次地融合在一起，对医学进行一次“创造性破坏”。在这超级融合之下，权力第一次交回到我们自己手中，而只有我们自己，才能真正将这场医学革命进行下去，颠覆医疗。