Spark and Python for Big Data with PySpark from Udemy

What's inside

Learning objectives

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!
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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!
Show more
Show less

Syllabus

Welcome to the course!

Introduction

Course Overview

Frequently Asked Questions

What is Spark? Why Python?

Learn how to set-up Python and Spark on your system!

Let's explain the set-up for the course!

Note on Installation Sections

Installation Option 3: Databricks Platform

Recommended Setup

Databricks Setup

Installation Option 1: VirtualBox Setup with Ubuntu

Let's walk through the local installation of Ubuntu

Local Installation VirtualBox Part 2

Setting up PySpark

Installation Option 2: AWS EC2

Let's show you how to use Amazon Web Services' EC2 Instances for Spark!

Creating the EC2 Instance

SSH with Mac or Linux

Installations on EC2

Installation Option 4: AWS EMR

AWS EMR Setup

Quickly get up to speed with Python

Introduction to Python Crash Course

Jupyter Notebook Overview

Python Crash Course Part One

Python Crash Course Part Two

Python Crash Course Part Three

Python Crash Course Exercises

Python Crash Course Exercise Solutions

Learn how to work with Spark DataFrames in Python!

Introduction to Spark DataFrames

Learn the basics of Spark DataFrames!

Spark DataFrame Basics Part Two

Learn some basic operations with Spark 2.0

Groupby and Aggregate Operations

Missing Data

Dates and Timestamps

Get some practice with Spark DataFrames!

DataFrame Project Exercise

DataFrame Project Exercise Solutions

Learn about Machine Learning and MLlib

Introduction to Machine Learning and ISLR

Machine Learning with Spark and Python with MLlib

Learn the basics of Linear Regression with Python and Spark!

Linear Regression Theory and Reading

Linear Regression Documentation Example

Regression Evaluation

Linear Regression Example Code Along

Linear Regression Consulting Project

Linear Regression Consulting Project Solutions

Learn how to use Logistic Regression for Classification!

Logistic Regression Theory and Reading

Logistic Regression Example Code Along

Logistic Regression Code Along

Logistic Regression Consulting Project

Logistic Regression Consulting Project Solutions

Learn how to utilize Decision Trees and Random Forests in Spark with Python!

Tree Methods Theory and Reading

Tree Methods Documentation Examples

Decision Tress and Random Forest Code Along Examples

Random Forest - Classification Consulting Project

Random Forest Classification Consulting Project Solutions

Learn how to use K-means to cluster unlabeled data!

K-means Clustering Theory and Reading

KMeans Clustering Documentation Example

Clustering Example Code Along

Clustering Consulting Project

Clustering Consulting Project Solutions

Learn how to use Spark's built-in collaborative filtering!

Introduction to Recommender Systems

Recommender System - Code Along Project

Learn how to use Python and Spark for Natural Language Processing!

Introduction to Natural Language Processing

NLP Tools Part One

NLP Tools Part Two

Natural Language Processing Code Along Project

Learn how to use Spark to work with streaming data.

Introduction to Streaming with Spark!

Spark Streaming Documentation Example

Spark Streaming Twitter Project - Part

Spark Streaming Twitter Project - Part Two

Spark Streaming Twitter Project - Part Three

Get special offers on other courses!

Bonus Lecture:

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suits professionals who want to combine Python with Big Data analysis for career advancement

May be less suited for beginners who lack prior programming or data analysis experience

Provides hands-on experience through exercises and Mock Consulting Projects

Covers advanced Spark technologies like Spark SQL, Spark Streaming, and Gradient Boosted Trees

Teaches the latest Spark 2.0 DataFrame syntax, keeping learners up-to-date with current industry practices

Leverages real-life scenarios through Consulting Projects, simulating professional problem-solving

Reviews summary

Decent pyspark introduction

According to students, Spark and Python for Big Data with PySpark is a decent way to get started with features of PySpark. While this course may be outdated, learners say it still offers a solid foundation and can be especially helpful for those new to PySpark.

Students who are new to PySpark have found this course to be especially helpful.

"This was a really great way to get an introduction to some interesting features of pyspark"

Some students feel the course content is outdated.

"it is a bit dated now"

"something about environment set up for a local VM was not quite as straightforward as the lecture on it"

"I believe this is also due to some things being outdated"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Spark and Python for Big Data with PySpark with these activities:

Apache Spark and Python Tutorial by Databricks

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Follow this tutorial to reinforce your understanding of Apache Spark and Python, the main technologies used in this course.

Browse courses on Apache Spark

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Follow the tutorial's instructions and complete all the exercises.

Join a Spark and Python Study Group

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Find a study group or start one yourself to discuss course material, work on projects together, and quiz each other.

Browse courses on Apache Spark

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Find or start a study group with fellow students.
Meet regularly to discuss course material.
Work on projects together.
Quiz each other on key concepts.

Spark and Python Coding Exercises

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Solve coding exercises to improve your proficiency in using Spark and Python for data analysis.

Browse courses on Apache Spark

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Find online coding exercises or create your own.
Solve the exercises using Spark and Python.
Review your solutions and identify areas for improvement.

Three other activities

Expand to see all activities and additional details

Show all six activities

Learning Spark: Lightning-Fast Data Analytics

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Read this book to gain a comprehensive understanding of Apache Spark and its applications in data analytics.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read the book thoroughly.
Take notes and highlight important concepts.
Complete the exercises and projects in the book.

Spark and Python Data Analysis Project

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Develop a data analysis project using Spark and Python to solidify your understanding of the technologies and their applications.

Browse courses on Apache Spark

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Define the scope and objectives of your project.
Gather and prepare your data.
Develop your Spark and Python code.
Analyze your results and draw conclusions.
Write a report or presentation on your project.

Build a Spark and Python Application

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Take your learning to the next level by building a real-world application using Spark and Python.

Browse courses on Apache Spark

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Identify a problem or opportunity that can be addressed with Spark and Python.
Design and develop your application.
Test and deploy your application.
Monitor and maintain your application.

Career center

Learners who complete Spark and Python for Big Data with PySpark will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, cleaning, and analyzing data. They work with large datasets to identify trends, patterns, and insights. Data Scientists may use Spark and Python to analyze big data and build machine learning models. This course can help you develop the skills and knowledge you need to become a successful Data Scientist. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models. They work with large datasets to train and test machine learning models. Machine Learning Engineers may use Spark and Python to analyze big data and build machine learning models. This course can help you develop the skills and knowledge you need to become a successful Machine Learning Engineer. You will learn how to use Spark DataFrames, MLlib, and other tools to build machine learning models. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Machine Learning Engineer

Data Management Analyst

Data Management Analysts are responsible for managing data assets. They work with large datasets to ensure that data is clean, accurate, and accessible. Data Management Analysts may use Spark and Python to analyze big data and build data management systems. This course can help you develop the skills and knowledge you need to become a successful Data Management Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Data Management Analyst

Data Analyst

Data Analysts play a key role in many industries, including finance, healthcare, technology, and retail. They work with large datasets to identify trends, patterns, and insights. Data Analysts may use Spark and Python to analyze big data, a skill that is in high demand in the tech sector. This course can help you develop the skills and knowledge you need to become a successful Data Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Data Analyst

Database Administrator

Database Administrators are responsible for managing databases. They work with large datasets to ensure that databases are running smoothly and efficiently. Database Administrators may use Spark and Python to analyze big data and build database systems. This course can help you develop the skills and knowledge you need to become a successful Database Administrator. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Database Administrator

Data Engineer

Data Engineers are responsible for building and maintaining data pipelines. They work with large datasets to ensure that data is clean, accurate, and accessible. Data Engineers may use Spark and Python to analyze big data and build machine learning models. This course can help you develop the skills and knowledge you need to become a successful Data Engineer. You will learn how to use Spark DataFrames, MLlib, and other tools to build data pipelines. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data. They work with large datasets to identify trends, patterns, and insights. Quantitative Analysts may use Spark and Python to analyze big data and build financial models. This course can help you develop the skills and knowledge you need to become a successful Quantitative Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze financial data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to solve business problems. They work with large datasets to identify trends, patterns, and insights. Operations Research Analysts may use Spark and Python to analyze big data and build optimization models. This course can help you develop the skills and knowledge you need to become a successful Operations Research Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use mathematical and statistical methods to analyze data. They work with large datasets to identify trends, patterns, and insights. Statisticians may use Spark and Python to analyze big data and build statistical models. This course can help you develop the skills and knowledge you need to become a successful Statistician. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Statistician

Software Engineer

Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies, including Python and Spark. Software Engineers may use Spark and Python to analyze big data and build machine learning models. This course can help you develop the skills and knowledge you need to become a successful Software Engineer. You will learn how to use Spark DataFrames, MLlib, and other tools to build software systems. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts use financial data to make investment decisions. They work with large datasets to identify trends, patterns, and insights. Financial Analysts may use Spark and Python to analyze big data and build financial models. This course can help you develop the skills and knowledge you need to become a successful Financial Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze financial data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts work with businesses to identify and solve problems. They use data and analysis to help businesses make better decisions. Business Analysts may use Spark and Python to analyze big data and identify business opportunities. This course can help you develop the skills and knowledge you need to become a successful Business Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers collect and analyze data about consumer behavior. They use data to identify trends, patterns, and insights. Market Researchers may use Spark and Python to analyze big data and identify marketing opportunities. This course can help you develop the skills and knowledge you need to become a successful Market Researcher. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

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Information Security Analyst

Information Security Analysts are responsible for protecting data from unauthorized access. They work with large datasets to identify security threats and vulnerabilities. Information Security Analysts may use Spark and Python to analyze big data and build security systems. This course can help you develop the skills and knowledge you need to become a successful Information Security Analyst. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

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Actuary

Actuaries use mathematical and statistical methods to assess risk. They work with large datasets to identify trends, patterns, and insights. Actuaries may use Spark and Python to analyze big data and build risk models. This course can help you develop the skills and knowledge you need to become a successful Actuary. You will learn how to use Spark DataFrames, MLlib, and other tools to analyze big data. You will also learn how to use Python to automate and streamline your work.

See salaries and explore the career path for Actuary