Course
Feature Engineering with PySpark
AdvancedSkill Level
Updated 01/2026SparkData Manipulation4 hr16 videos60 Exercises5,000 XP17,615Statement of Accomplishment
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Prerequisites
Supervised Learning with scikit-learnIntroduction to PySpark1
Exploratory Data Analysis
Get to know a bit about your problem before you dive in! Then learn how to statistically and visually inspect your dataset!
2
Wrangling with Spark Functions
Real data is rarely clean and ready for analysis. In this chapter learn to remove unneeded information, handle missing values and add additional data to your analysis.
3
Feature Engineering
In this chapter learn how to create new features for your machine learning model to learn from. We'll look at generating them by combining fields, extracting values from messy columns or encoding them for better results.
4
Building a Model
In this chapter we'll learn how to choose which type of model we want. Then we will learn how to apply our data to the model and evaluate it. Lastly, we'll learn how to interpret the results and save the model for later!
Feature Engineering with PySpark
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FAQs
What prior experience do I need with PySpark and machine learning?
You should know PySpark basics, pandas, SQL fundamentals, introductory statistics in Python, and supervised learning with scikit-learn before taking this advanced course.
What feature engineering techniques are covered in this course?
You will learn exploratory data analysis, data wrangling with Spark functions, handling missing values, building machine learning pipelines, and creating features for big data models.
Why use PySpark instead of pandas for feature engineering?
PySpark handles datasets too large to fit in memory on a single machine. This course teaches feature engineering at scale for big data problems that pandas cannot handle efficiently.
Does the course cover building end-to-end ML pipelines in PySpark?
Yes. The final chapter focuses on building machine learning pipelines that combine feature transformations with model training, creating reproducible workflows in PySpark.
How many exercises and how much time should I plan for?
The course has 81 exercises across four chapters. Most learners complete it in about four to five hours, reflecting the depth of the material covered.
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