In data science, data is called "big" if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
Requirements
Python for Data Science Probability and Statistics in Data Science using Python Machine Learning Fundamentals
What I will learn?
Identifying the computational tradeoffs in Spark application.
