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Learn PySpark, earn certificates with paid and free online courses from Imperial College London, UC Berkeley and other top universities around the world. Read reviews to decide if a class is right for you.
Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!
Master PySpark and Big Data analytics with this concise course. Learn data handling, aggregation, filtering, and more in just 1-2 hours.
Master Apache Spark using Spark SQL as well as PySpark with Python3 with complementary lab access
Mastering AWS & PySpark: Spark, PySpark, AWS, Spark Ecosystem, Hadoop, and Spark Applications [AWS, Hadoop, Pyspark]
Learn to create big data streaming pipelines with Apache Spark using Python. Analyze live Tweet data, integrate with Apache Kafka, and master Spark's latest features. Offered by Udemy.
Big data Python Spark PySpark coding framework logging error handling unit testing PyCharm PostgreSQL Hive data pipeline
Learn PySpark, an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Get up and running with Apache Spark quickly. This practical hands-on course shows Python users how to work with Apache PySpark to leverage the power of Spark for data science.
In this demo, you'll learn how to get data into PySpark.
This syllabus covers an introduction to Pyspark, DataFrames, Mlib, Databricks, and Linear Regression In-Depth Maths Intuition.
Learn to unify single-host and distributed deep learning using Maggy with Databricks. Master the transition from Python to PySpark in under an hour.
Explore the latest updates in PySpark with Apache Spark 3.3 and beyond in this less than 1-hour material from Databricks. Learn about new functionalities, performance, and future roadmap.
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