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Apache Spark for Data Scientists
11 Courses
5 Labs
9 Hours
Skill IQ
Apache Spark is a powerful, open-source framework that enables data scientists to efficiently process, analyze, and transform large-scale data. This learning path equips you with the essential skills necessary to perform data science tasks using Apache Spark. In this path, you'll learn everything from Spark fundamentals, DataFrames, and SQL to advanced data transformations, machine learning, and structured streaming.
Content in this path
Apache Spark for Data Scientists
Start watching the courses below to get started.
Hands-on Practice with Apache Spark
The following labs will help you get practical experience with Apache Spark.
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What You'll Learn
- Apache Spark Architecture and Fundamentals
- How to work with RDDs, DataFrames, and datasets in Spark
- How to transform data in Spark using PySpark, Spark SQL, and the Pandas API
- How to process real-time data streams using structured streaming in Spark
- How to use window and join operations in Spark
- How to optimize performance in Spark
- How to monitor Spark clusters
- How to perform machine learning operations in Spark
- How to use graph operations in Spark
Prerequisites
- Learners interested in this path should have basic programming knowledge in Python or Scala. Learners should also be familiar with SQL and fundamental data processing, machine learning, and big data concepts.
Related topics
- Apache Spark
- Data Science
- Big Data
- Machine Learning
- Data Transformation
- SQL
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