Feature Engineering with PySpark

Fortgeschritten

Aktualisierte 05.2025

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

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Kursbeschreibung

The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering. With size of datasets now becoming ever larger, let's use PySpark to cut this Big Data problem down to size!

Voraussetzungen

Supervised Learning with scikit-learn Introduction to PySpark

Exploratory Data Analysis

Kapitel starten

Where to Begin

Kursbeschreibung

Leistungsnachweis verdienen

Mach mit .css-nklxlk{color:var(--wf-brand--main, #03EF62);}16 Millionen Lernende und starte Feature Engineering with PySpark heute!

Kostenloses Konto erstellen

Mach mit 16 Millionen Lernende und starte Feature Engineering with PySpark heute!