[研討會心得][PyCon APAC 2014] 05/17 Day 1 note
Python-powered Analysts @WesMackinn Author for “Python for Data Analysis” Python’s role in 2014 for analysis BI Query Report ordinary Alert when trend change ETL (Extract Transform Load) moving data between storage format Normalization How did we get here? Good develop tool — iPython Learning resource: nbviewer.ipython.org static html share space The libraries packaging tech become more maturing. Why did pandas succeed? tasteful and consistent solutions passionate user base An API optimized for terminal-freindliness composability Tools before Data Analysis Cython NumPy matplotlib ipython Python might not best language for Data Analysis, but everyone can using python to discussion. Scikit-learn http://scikit-learn.org/stable/ Machine learning in python. Pandas: The good and bad Ecosystem compatibility Design for broad appeal The big data problem. Big Data Trend Python/R/Julia growing JVM-base big data ecosystem Concurrent / multicore programming challenges Staying competitive “Enterprise money” is still stay in JVM-Ecpsystem A bit about DataPad http://www.datapad.io/ Exploratory analytics and report-building environment Powerful analysis tool No...
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