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DataCamp

Big Data with PySpark

via DataCamp

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Overview

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Advance your data skills by mastering Apache Spark. Using the Spark Python API, PySpark, you will leverage parallel computation with large datasets, and get ready for high-performance machine learning. From cleaning data to creating features and implementing machine learning models, you'll execute end-to-end workflows with Spark. The track ends with building a recommendation engine using the popular MovieLens dataset and the Million Songs dataset.

Syllabus

  • Introduction to PySpark
    • Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
  • Big Data Fundamentals with PySpark
    • Learn the fundamentals of working with big data with PySpark.
  • Cleaning Data with PySpark
    • Learn how to clean data with Apache Spark in Python.
  • Feature Engineering with PySpark
    • Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
  • Machine Learning with PySpark
    • Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
  • Building Recommendation Engines with PySpark
    • Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
  • Building a Demand Forecasting Model

Taught by

Nick Solomon, Lore Dirick, John Hogue, Shantanu Trivedi, Upendra Kumar Devisetty, Andrew Collier, and Mike Metzger

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