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. You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib). In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
* This article was originally published here
advertiser arms availabile belt blue boot breed bridge brooches buckle case claddagh color com comfort crafted cross crown custom-made detail dress elegant ericdress eur faces features fit foods frame free gift hand home id intensive internet irish iron jeans jk knitted li light london love manufacturer masonic material miso name native noses oakley open oz patriot pencil perfect plain plant plus post-lift posts price products rainbow red redlinegoods redtab retail romwe rounded sale scoop sexy shift size sleeve sourced spring stainless star steel store stripes styles suit sustained tailored-fit threads top types ul universal usd von wholesale wide women xl
created at TagCrowd.com