Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

University of California, San Diego

Big Data Analytics Using Spark

University of California, San Diego via edX

Overview

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.

Taught by

Yoav Freund

Related Courses

Reviews

Start your review of Big Data Analytics Using Spark

Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free