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Big Data Management and Analysis

Course Number

STSCI 5065

Prerequisites:

Knowledge of a general purpose computer programming language, such as JAVA, Python, Ruby, or C++, or at least taking STSCI 4060 in parallel with this course; STSCI 5060 or basic SQL knowledge; STSCI 5010 or basic knowledge of SAS programming; STSCI 3520 or STSCI 4030 or basic knowledge of R programming.

Permission of instructor required.

Enrollment preference given to: students in the MPS Program in Applied Statistics. 

This course covers the concepts, challenges, industry trends, management and analysis of big data using the Hadoop system. Topics include: basics of the Apache Hadoop platform and Hadoop ecosystem; the Hadoop distributed file system (HDFS); MapReduce or its alternative, a parallel programming model for distributed processing of large data sets; common big data tools, such as Pig (a procedural data processing language for Hadoop parallel computation), Hive (a declarative SQL-like language to handle Hadoop jobs), HBase (the most popular NoSQL database), and YARN; case studies; and  integration of Hadoop with statistical software packages, e.g., SAS and R. 

Instructor

  • Xiaolong Yang

Course Semesters

Spring

Course Credit Hours

3
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