Learn how to analyze large datasets using Jupyter notebooks, MapReduce and Spark as a platform. 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.

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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.

What you'll learn

- Programming Spark using Pyspark

- Identifying the computational tradeoffs in a Spark application

- Performing data loading and cleaning using Spark and Parquet

- Modeling data through statistical and machine learning methods

Prerequisites

The previous courses in the MicroMasters program: DSE200x - Python for Data Science, DSE210x - Probability and Statistics in Data Science using Python and DSE220x - Machine Learning Fundamentals.

Data Science MicroMasters

Machine Learning Library

MLlib