Big Data with PySpark

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.

Python24 hours6 coursesStatement of Accomplishment

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Introduction to PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

4 hours

Big Data Fundamentals with PySpark

Learn the fundamentals of working with big data with PySpark.

4 hours

Cleaning Data with PySpark

Learn how to clean data with Apache Spark in Python.

4 hours

Feature Engineering with PySpark

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

4 hours

Machine Learning with PySpark

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

4 hours

Building Recommendation Engines with PySpark

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

4 hours

Big Data with PySpark

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Join over .css-1osvizq-TrackShowPage{color:#03ef62;}12,820,000 learners and start Big Data with PySpark today!

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Join over 12,820,000 learners and start Big Data with PySpark today!