Course: Computational Techniques for Large-Scale Data Analysis (CSE 491/891)

Project Details

Project Lead
Pang-Ning Tan 
Project Manager
Pang-Ning Tan 
Project Members
Dirk Colbry, Jianpeng Xu, Philip Plachta, Clay Reimann, Ruijuan He, Lei Huang, Yi Zhang, Stephen Paslaski, Di Dan, Maxime Goovaerts, Liyan Wang, Joshua Willard, Christian Fincher, Yevgeny Khessin, Ryan Westra, Mark Schwerzler, Matthew Wenner, Yue Zhuang  
Institution
Michigan State University, Computer Science & Engineering  
Discipline
Computer Science (401) 

Abstract

The new millennium has ushered in the era of big data and data-intensive computing. As storage becomes cheaper and computers become more powerful, the need for advanced computing solutions to address large-scale data analysis problems has become increasingly important. This course is intended for senior undergraduate and graduate students who are interested in gaining hands-on experience applying computational techniques to solve large-scale data analysis problems.

Intellectual Merit

This course is intended for senior undergraduate and graduate students who are interested in gaining hands-on experience applying Hadoop to analyze large-scale data.

Broader Impacts

Students will have practical experience writing, debugging, compiling, and executing programs that can run on a Hadoop cluster.

Scale of Use

There are currently 44 students enrolled in the class, each will have to run their own Hadoop instance. There will be 2 homework assignments and 1 class project that requires Hadoop.