Big Data with PySpark

Обновлено 03.2026

Master how to process big data and leverage it efficiently with Apache Spark using the PySpark API.

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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.

Предварительные требования

Для участия в этом курсе никаких предварительных требований нет.

Course
1
Introduction to PySpark
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
Course
3
Big Data Fundamentals with PySpark
Learn the fundamentals of working with big data with PySpark.
Course
4
Cleaning Data with PySpark
Learn how to clean data with Apache Spark in Python.
Course
5
Feature Engineering with PySpark
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Course
6
Machine Learning with PySpark
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Course
7
Building Recommendation Engines with PySpark
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Project
Бонус
Building a Demand Forecasting Model
Use PySpark to build an e-commerce forecasting model!