Big Data Analysis with Apache Spark

Learn how to apply data science techniques using parallel programming in Apache Spark to explore big data.

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Created by: Anthony D. Joseph

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Course Description

Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term data science'.
This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.

Intermediate Level

Time: 4 Weeks

Instructor Details

Anthony D. Joseph

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Anthony D. Joseph is a Professor in Electrical Engineering and Computer Science at UC Berkeley. He received his B.S., S.M., and Ph.D. Degrees in Computer Science from MIT. He joined the UC Berkeley faculty in 1998, where he is developing adaptive techniques for: cloud computing, network and computer security, and security defenses for machine learning-based decision systems. He also co-leads the DETERlab testbed, a secure scalable testbed for conducting cybersecurity research, and he is a Technical Advisor at Databricks.