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Big Data Fundamentals with PySpark

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Course Report - Big Data Fundamentals with PySpark

Course Report

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Big Data has attracted a lot of attention in the past few years, and it is now a mainstream topic for many companies. What is Big Data? This course will introduce you to the basics of Big Data with PySpark. Spark is a framework which allows for Big Data "lightning fast cluster computing". It's a data processing platform engine that can run programs up to 100x faster in memory than Hadoop and 10x faster on disk. PySpark allows Spark programming. It includes powerful libraries such as SparkSQL for machine learning and MLlib to facilitate programming. Learn about William Shakespeare and how to analyze Fifa 2018 data. You'll also learn about cluster genomic datasets. This course will give you a solid understanding of PySpark and its general application to Big Data analysis.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

Introduction to Python

What You Will Learn

Learn the fundamentals of working with big data with PySpark

You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets

At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis

Course Instructors

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Upendra Kumar Devisetty

Science Analyst at CyVerse

Upendra Kumar Devisetty is a Science Analyst at CyVerse where he scientifically interacts with biologists, bioinformaticians, programming teams and other members of CyVerse team. He also coordinates ...

Course Reviews

Average Rating Based on 3 reviews

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