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Big Data Analytics Using Spark

This course is a part of Data Science MicroMasters® Program, a 5-course MicroMasters series from edX.

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.

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The University of California San Diego

Rating Not enough ratings
Length 10 weeks
Effort 9 - 12 hours per week
Starts Aug 25 (next week)
Cost $350
From The University of California San Diego, UC San DiegoX, UCSanDiegoX via edX
Instructor Yoav Freund
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics

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edX

&

The University of California San Diego

Rating Not enough ratings
Length 10 weeks
Effort 9 - 12 hours per week
Starts Aug 25 (next week)
Cost $350
From The University of California San Diego, UC San DiegoX, UCSanDiegoX via edX
Instructor Yoav Freund
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Analysis & Statistics

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