STSCI 5065
Last Updated
- Schedule of Classes - June 22, 2015 4:42PM EDT
- Course Catalog - June 11, 2015 6:21PM EDT
Classes
STSCI 5065
Course Description
Course information provided by the Courses of Study 2014-2015.
Concepts, challenges, and industry trends of big data, with a focus on 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.
When Offered Spring.
Permission Note Enrollment preference given to: students in the MPS Program in Applied Statistics.
Prerequisites/Corequisites 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.
Regular Academic Session. Choose one lecture and one laboratory.
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Credits and Grading Basis
3 Credits Stdnt Opt(Student Option)
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Class Number & Section Details
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Meeting Pattern
- MW Space Sciences Building 105
Instructors
Yang, X
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Additional Information
Prerequisites: Knowledge of a general purpose computer programming language, such as JAVA, Python, Ruby, or C++, or at least taking STSC 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.
Instructor Consent Required (Add)
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Class Number & Section Details
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Meeting Pattern
- F Mann Library B30A
Instructors
Yang, X
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Additional Information
Instructor Consent Required (Add)
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