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This course explores the big data ecosystem, focusing on hands-on utilization of PySpark—the Python API for Apache Spark. 

In this course, you’ll experience a balanced blend of theory and practice. You’ll learn about data ingestion, storage, distributed computing, PySpark’s intricacies, data processing, data analysis, performance optimization, tool integration, and practical applications like machine learning. 

This course, suited for beginners to intermediate learners, will give you an understanding of big data tools and techniques. After completing this course, you’ll be fully equipped with effective problem-solving capabilities in real-world scenarios.

Mastering Big Data with PySpark

**(Fill in the blank.)** Regression is a type of supervised learning that deals with ________.

(Fill in the blank.) Regression is a type of supervised learning that deals with ________.

It's a linear data structure to store sequential operations on data.



It's used for constructing, chaining, and parameterizing multiple machine learning algorithms into a single workflow.



It's a data preprocessing technique for handling missing values in Spark DataFrames.



It's a visualization tool for interpreting machine learning models in PySpark.



Which best describes the purpose of `Pipeline` in PySpark MLlib?

Which best describes the purpose of <code>Pipeline</code> in PySpark MLlib?

Which is a feature extraction technique in PySpark MLlib?

Which is a feature extraction technique in PySpark MLlib?

Which is a feature transformation technique in PySpark MLlib?

Which is a feature transformation technique in PySpark MLlib?

Which is used to convert categorical variables into numerical representation in PySpark MLlib?

Which is used to convert categorical variables into numerical representation in PySpark MLlib?

Machine learning with PySpark 

Machine learning with PySpark

Test your knowledge of machine learning with PySpark.

Introduction to the Course

Introduction to Big Data

Exploring PySpark Core and RDDs

PySpark DataFrames and SQL

Customer Churn Analysis Using PySpark

Machine Learning with PySpark

Modeling with PySpark MLlib

Predicting Diabetes in Patients Using PySpark MLlib

Performance Optimization in PySpark

PySpark Optimization: Analyzing NYC Restaurants Data

Integrating PySpark with Other Big Data Tools

Wrap Up

Quiz: Machine Learning with PySpark