95-869 Big Data and Large-scale Computing
6 units, Carnegie Mellon University
The rate and amount of data being generated in todays world by both humans and machines are unprecedented. Being able to store, manage, and analyze largescale data has critical impact on business intelligence, scientific discovery, social and environmental challenges. The goal of this course is to equip students with the understanding, knowledge, and practical skills to develop big data machine learning solutions with the stateoftheart tools, particularly those in the Spark environment, with a focus on programming models in MLlib, GraphX, and SparkSQL. See the syllabus for more details. Students will also gain handson experience with MapReduce and Apache Spark using realworld datasets. This course is designed to give a graduatelevel student a thorough grounding in the technologies and best practices used in big data machine learning. The course assumes that the students have the understanding of basic data analysis and machine learning concepts as well as basic knowledge of programming preferably in Python or Java. Previous experience with Hadoop, Spark or distributed computing is NOT required. Students cannot take this course as passno pass.
Prerequisites: none listed
Courseweave needs JavaScript to show sections, workloads and planning tools.