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Comprehensive Learning Path–data Science in PythonJourney from a python noob to a kaggler on PythonSo, you want to become a data scientist or May is you is already one and want to expand your tool repository. You are landed at the right place. The aim of this page was to provide a comprehensive learning path to people new to Python for

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Reprint: http://www.cnblogs.com/zhijianliutang/p/4050931.htmlObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft

ObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data Mining algorithm summary seria

Charter where we get to own the end to end user experience for big data. we get exposure to users and the inter-workings of the massively parallel systems. most importantly, we are producing the differentiator that will validate Microsoft's big data products against its competitors. This is a fun and fast paced environment and a great opportunity to work on something highly strategic to

Windows? Extensive knowledge and experience in on least one of the following Areas:data mining, web Mining, machine learning, stat Istics, Business Intelligence/customer Intelligence, user modeling, information retrieval, databases, data warehousing, O LAP, data Processing (ETL), e-metrics/measurement, Parallel and distributed computation (both or more areas is preferred).? Superior communication skills, b

Windows? Extensive knowledge and experience in on least one of the following Areas:data mining, web Mining, machine learning, stat Istics, Business Intelligence/customer Intelligence, user modeling, information retrieval, databases, data warehousing, O LAP, data Processing (ETL), e-metrics/measurement, Parallel and distributed computation (both or more areas is preferred).? Superior communication skills, b