Online Course
Big Data Analytics Using Spark
University of California, San Diego via edX
-
82
-
- Write review
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
-
Big Data Analytics
-
Big Data Analysis with Scala and Spark
École Polytechnique Fédérale de Lausanne
3.0 -
Graph Analytics for Big Data
University of California, San Diego
2.5 -
Big Data applications and Analytics
Indiana University
3.0 -
Big Data Analytics
Queensland University of Technology
-
Big Data Analytics in Healthcare
Georgia Institute of Technology
Reviews
0.0 rating, based on 0 reviews