Spark and Python for Big Data with PySpark

Spark and Python for Big Data with PySpark, available at $159.99, has an average rating of 4.5, with 67 lectures, based on 24443 reviews, and has 136661 subscribers.

You will learn about Use Python and Spark together to analyze Big Data Learn how to use the new Spark 2.0 DataFrame Syntax Work on Consulting Projects that mimic real world situations! Classify Customer Churn with Logisitic Regression Use Spark with Random Forests for Classification Learn how to use Spark's Gradient Boosted Trees Use Spark's MLlib to create Powerful Machine Learning Models Learn about the DataBricks Platform! Get set up on Amazon Web Services EC2 for Big Data Analysis Learn how to use AWS Elastic MapReduce Service! Learn how to leverage the power of Linux with a Spark Environment! Create a Spam filter using Spark and Natural Language Processing! Use Spark Streaming to Analyze Tweets in Real Time! This course is ideal for individuals who are Someone who knows Python and would like to learn how to use it for Big Data or Someone who is very familiar with another programming language and needs to learn Spark It is particularly useful for Someone who knows Python and would like to learn how to use it for Big Data or Someone who is very familiar with another programming language and needs to learn Spark.

Enroll now: Spark and Python for Big Data with PySpark

Summary

Title: Spark and Python for Big Data with PySpark

Price: $159.99

Average Rating: 4.5

Number of Lectures: 67

Number of Published Lectures: 67

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Use Python and Spark together to analyze Big Data
Learn how to use the new Spark 2.0 DataFrame Syntax
Work on Consulting Projects that mimic real world situations!
Classify Customer Churn with Logisitic Regression
Use Spark with Random Forests for Classification
Learn how to use Spark's Gradient Boosted Trees
Use Spark's MLlib to create Powerful Machine Learning Models
Learn about the DataBricks Platform!
Get set up on Amazon Web Services EC2 for Big Data Analysis
Learn how to use AWS Elastic MapReduce Service!
Learn how to leverage the power of Linux with a Spark Environment!
Create a Spam filter using Spark and Natural Language Processing!
Use Spark Streaming to Analyze Tweets in Real Time!

Who Should Attend

Someone who knows Python and would like to learn how to use it for Big Data
Someone who is very familiar with another programming language and needs to learn Spark

Target Audiences

Someone who knows Python and would like to learn how to use it for Big Data
Someone who is very familiar with another programming language and needs to learn Spark

Learn the latest Big Data Technology – Spark! And learn to use it with one of the most popular programming languages, Python!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA,and more are all using Sparkto solve their big data problems!

Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame�framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion!

If you’re ready to jump into the world of Python, Spark, and Big Data, this is the course for you!

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction

Lecture 2: Course Overview

Lecture 3: Frequently Asked Questions

Lecture 4: What is Spark? Why Python?

Chapter 2: Setting up Python with Spark

Lecture 1: Set-up Overview

Lecture 2: Note on Installation Sections

Chapter 3: Databricks Setup

Lecture 1: Recommended Setup

Lecture 2: Databricks Setup

Chapter 4: Local VirtualBox Set-up

Lecture 1: Local Installation VirtualBox Part 1

Lecture 2: Local Installation VirtualBox Part 2

Lecture 3: Setting up PySpark

Chapter 5: AWS EC2 PySpark Set-up

Lecture 1: AWS EC2 Set-up Guide

Lecture 2: Creating the EC2 Instance

Lecture 3: SSH with Mac or Linux

Lecture 4: Installations on EC2

Chapter 6: AWS EMR Cluster Setup

Lecture 1: AWS EMR Setup

Chapter 7: Python Crash Course

Lecture 1: Introduction to Python Crash Course

Lecture 2: Jupyter Notebook Overview

Lecture 3: Python Crash Course Part One

Lecture 4: Python Crash Course Part Two

Lecture 5: Python Crash Course Part Three

Lecture 6: Python Crash Course Exercises

Lecture 7: Python Crash Course Exercise Solutions

Chapter 8: Spark DataFrame Basics

Lecture 1: Introduction to Spark DataFrames

Lecture 2: Spark DataFrame Basics

Lecture 3: Spark DataFrame Basics Part Two

Lecture 4: Spark DataFrame Basic Operations

Lecture 5: Groupby and Aggregate Operations

Lecture 6: Missing Data

Lecture 7: Dates and Timestamps

Chapter 9: Spark DataFrame Project Exercise

Lecture 1: DataFrame Project Exercise

Lecture 2: DataFrame Project Exercise Solutions

Chapter 10: Introduction to Machine Learning with MLlib

Lecture 1: Introduction to Machine Learning and ISLR

Lecture 2: Machine Learning with Spark and Python with MLlib

Chapter 11: Linear Regression

Lecture 1: Linear Regression Theory and Reading

Lecture 2: Linear Regression Documentation Example

Lecture 3: Regression Evaluation

Lecture 4: Linear Regression Example Code Along

Lecture 5: Linear Regression Consulting Project

Lecture 6: Linear Regression Consulting Project Solutions

Chapter 12: Logistic Regression

Lecture 1: Logistic Regression Theory and Reading

Lecture 2: Logistic Regression Example Code Along

Lecture 3: Logistic Regression Code Along

Lecture 4: Logistic Regression Consulting Project

Lecture 5: Logistic Regression Consulting Project Solutions

Chapter 13: Decision Trees and Random Forests

Lecture 1: Tree Methods Theory and Reading

Lecture 2: Tree Methods Documentation Examples

Lecture 3: Decision Tress and Random Forest Code Along Examples

Lecture 4: Random Forest – Classification Consulting Project

Lecture 5: Random Forest Classification Consulting Project Solutions

Chapter 14: K-means Clustering

Lecture 1: K-means Clustering Theory and Reading

Lecture 2: KMeans Clustering Documentation Example

Lecture 3: Clustering Example Code Along

Lecture 4: Clustering Consulting Project

Lecture 5: Clustering Consulting Project Solutions

Chapter 15: Collaborative Filtering for Recommender Systems

Lecture 1: Introduction to Recommender Systems

Lecture 2: Recommender System – Code Along Project

Chapter 16: Natural Language Processing

Lecture 1: Introduction to Natural Language Processing

Lecture 2: NLP Tools Part One

Lecture 3: NLP Tools Part Two

Lecture 4: Natural Language Processing Code Along Project

Chapter 17: Spark Streaming with Python

Lecture 1: Introduction to Streaming with Spark!

Lecture 2: Spark Streaming Documentation Example

Lecture 3: Spark Streaming Twitter Project – Part

Lecture 4: Spark Streaming Twitter Project – Part Two

Lecture 5: Spark Streaming Twitter Project – Part Three

Chapter 18: Bonus

Lecture 1: Bonus Lecture:

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 190 votes
2 stars: 314 votes
3 stars: 2105 votes
4 stars: 9214 votes
5 stars: 12617 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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