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Big Data com PySpark

Aprimore suas habilidades com dados dominando o Apache Spark. Usando a API Spark Python, PySpark, você aproveitará a computação paralela com grandes conjuntos de dados e se preparará para o aprendizado de máquina de alto desempenho. Desde a limpeza de dados até a criação de recursos e a implementação de modelos de aprendizado de máquina, você executará fluxos de trabalho de ponta a ponta com o Spark. O curso termina com a criação de um mecanismo de recomendação usando o popular conjunto de dados MovieLens e o conjunto de dados Million Songs.

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Amado por alunos de milhares de empresas

1
Python
Introdução ao PySpark

4 hours

Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School

2
Python
Big Data Fundamentals with PySpark

Learn the fundamentals of working with big data with PySpark.

4 hours

Upendra Kumar Devisetty Headshot

Upendra Kumar Devisetty

Science Analyst at CyVerse

3
Python
Cleaning Data with PySpark

Learn how to clean data with Apache Spark in Python.

4 hours

Mike Metzger Headshot

Mike Metzger

Data Engineer Consultant @ Flexible Creations

4
Python
Feature Engineering with PySpark

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

4 hours

John Hogue Headshot

John Hogue

Lead Data Scientist, General Mills

5
Python
Machine Learning with PySpark

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

4 hours

Andrew Collier Headshot

Andrew Collier

Data Scientist @ Exegetic Analytics

6
Python
Building Recommendation Engines with PySpark

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

4 hours

Jamen Long Headshot

Jamen Long

Data Scientist

7
Python
Building a Demand Forecasting Model

Use PySpark to build an e-commerce forecasting model!

2 hours

Caroline Barbosa Headshot

Caroline Barbosa

Senior Data Scientist

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