Big Data with PySpark

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

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Track Description

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.

Prerequisites

There are no prerequisites for this track

Course
1
Introduction to PySpark
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Course
2
Big Data Fundamentals with PySpark
Learn the fundamentals of working with big data with PySpark.
Course
3
Cleaning Data with PySpark
Learn how to clean data with Apache Spark in Python.
Course
4
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
5
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
6
Building Recommendation Engines with PySpark
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Project
bonus
Building a Demand Forecasting Model
Use PySpark to build an e-commerce forecasting model!