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Big Data with PySpark

Updated 03/2026
Master how to process big data and leverage it efficiently with Apache Spark using the PySpark API.
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PythonImporting & Cleaning Data25 hr8,199

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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

    Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!

  • 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.

  • Project

    bonus

    Building a Demand Forecasting Model

    Use PySpark to build an e-commerce forecasting model!

Big Data with PySpark
6 Courses
Track
Complete

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FAQs

Is this Track suitable for beginners?

No, prior knowledge of machine learning and Python is assumed if you start this track.

What is the programming language of this Track?

The programming language of this Track is Python.

Which jobs will benefit from this Track?

Data analysts, data engineers, and machine learning engineers will benefit from this Track.

How will this Track prepare me for my career?

This Track will prepare you for your career by teaching you essential skills and techniques required to work with large datasets and build machine learning models using PySpark.

How long does it take to complete this Track?

It usually takes 24 hours to complete this Track, but it can vary depending on the individual's pace.

What's the difference between a skill track and a career track?

A skill track is designed to focus on specific skills, while a career track is designed to provide a comprehensive learning experience for a specific job or career path.

What are the tasks included in this Track?

The tasks included in this Track are Introduction to PySpark, Big Data Fundamentals with PySpark, Cleaning Data with PySpark, Feature Engineering with PySpark, Machine Learning with PySpark, and Building Recommendation Engines with PySpark.

What datasets will be used in this Track?

The popular MovieLens dataset and the Million Songs dataset will be used in this Track for building a recommendation engine.

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