In this training course, you’ll learn how to use Apache Spark to perform exploratory data analysis (EDA), develop machine learning pipelines, and use the APIs and algorithms available in the Spark MLlib DataFrames API. You’ll also cover parallelizing machine learning algorithms at a conceptual level.
The workshop takes a pragmatic approach, with a focus on using Apache Spark for data analysis and building models using MLlib, while limiting the time spent on machine learning theory and the internal workings of Spark. You’ll work through examples using public datasets to learn how to apply Apache Spark to help you iterate faster and develop models on massive datasets and how to use familiar Python libraries with Spark’s distributed and scalable engine. You’ll leave with the tools and knowledge you need to get started using Spark for practical data analysis tasks and machine learning problems, as well as a firm understanding of DataFrames, the DataFrames MLlib API, and related documentation.
Topics covered include:
Joseph has over ten years of experience teaching and over five years of experience data science and analytics. He has taught in over a dozen countries around the world and been featured on Japanese television and Saudi newspapers. He holds a BS in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA with a focus in analytics from Bentley University. Previous to joining Databricks, Joseph was an instructor with Cloudera and Technical Sales Engineer with IBM. He is a rabid Arsenal FC supporter and competitive Magic: The Gathering player. He lives with his wife and daughter in Needham, MA.
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