Begin Your Journey with PySpark

Gain expertise in big data processing with PySpark. Learn how to manage and analyze large datasets, work with Spark's DataFrames and RDDs, and build scalable data pipelines for machine learning applications.

PySpark Course

Description

PySpark Training Curriculum for Data Enthusiasts

The PySpark course is designed for individuals eager to explore big data processing and analytics. Our curriculum covers essential concepts of Apache Spark using Python, teaching you how to work with large datasets, build data pipelines, and apply machine learning algorithms efficiently. This course is ideal for data analysts, engineers, and professionals aiming to enhance their data processing skills in real-world scenarios.

This course includes

  • 30 hrs Instructor-Led Training & Hands-On Project Work
  • Job Assistance
  • Mentor Support for Real-World Applications
  • Certificate of Completion

Course Content

Introduction to PySpark

  • What is PySpark?
  • Understanding Apache Spark
  • Setting Up the PySpark Environment
  • Introduction to Big Data Processing

PySpark Basics

  • RDDs (Resilient Distributed Datasets)
  • DataFrames and Datasets
  • Data Types in PySpark
  • Loading and Saving Data

Data Manipulation with PySpark

  • DataFrame Operations
  • Handling Missing Data
  • Filtering, Sorting, and Grouping Data
  • Aggregations and Joins

Machine Learning with PySpark

  • Introduction to MLlib
  • Feature Engineering
  • Building and Evaluating Models
  • Model Tuning and Optimization

Advanced PySpark

  • Using Spark SQL
  • Working with Streaming Data
  • Optimizing PySpark Applications
  • Deploying PySpark Workflows

PySpark Project Work

  • End-to-End Data Processing Project
  • Implementing Machine Learning Models
  • Optimizing Data Pipelines
  • Building a Real-Time Data Processing Application

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