Legend: Palais des congrès de Montréal = CC, Le Westin Montréal = W, Intercontinental Montréal = I
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_07C | Sun, 8/4/2013, 8:30 AM - 5:00 PM | W-Ville-Marie | |
| Statistical Computing for Big Data — Continuing Education Course | |||
| ASA , Section on Statistical Learning and Data Mining | |||
| Instructor(s): Liang Zhang, LinkedIn, Deepak Agarwal, LinkedIn | |||
| Massive data gets generated, stored and analyzed every day in various fields like bioinformatics, climatology, internet, telecommunications, and many more. Hadoop, as a distributed file storage and computing system, has become the most popular distributed system in the world. Statistical methods for analyzing such large scale data sets have become a challenging research area. The objective of this tutorial is to provide a detailed introduction of the open-source Hadoop system that uses Map-Reduce framework, and more importantly, to illustrate the use of Map-Reduce and Hadoop for real statistical applications, starting from basics like computing mean and variances, to more complicated scenarios such as fitting a large scale logistic regression on hundreds of gigabytes of data. Through this tutorial, the audience will learn Hadoop and Map-Reduce as a tool for statistical analysis and contribute to the research of statistical methods for big data. No prior knowledge of Hadoop or Map-Reduce is required. | |||
2013 JSM Online Program Home
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