Big Data Analytics Using Spark

This course will be retired on April 4, 2026. Last day to enroll is February 2, 2026, at 00:00 UTC. This course is intended only for learners enrolled into the former Data Science MicroMaster's Program 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.

• 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

• The previous courses in the MicroMasters program: DSE200x,DSE210x and DSE220x