Mastering Big Data Analytics with PySpark

Mastering Big Data Analytics with PySpark, available at $59.99, has an average rating of 4.4, with 41 lectures, 9 quizzes, based on 54 reviews, and has 428 subscribers.

You will learn about Gain a solid knowledge of vital Data Analytics concepts via practical use cases Create elegant data visualizations using Jupyter Run, process, and analyze large chunks of datasets using PySpark Utilize Spark SQL to easily load big data into DataFrames Create fast and scalable Machine Learning applications using MLlib with Spark Perform exploratory Data Analysis in a scalable way Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming This course is ideal for individuals who are This course will greatly appeal to data science enthusiasts, data scientists, or anyone who is familiar with Machine Learning concepts and wants to scale out his/her work to work with big data. or If you find it difficult to analyze large datasets that keep growing, then this course is the perfect guide for you! It is particularly useful for This course will greatly appeal to data science enthusiasts, data scientists, or anyone who is familiar with Machine Learning concepts and wants to scale out his/her work to work with big data. or If you find it difficult to analyze large datasets that keep growing, then this course is the perfect guide for you!.

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Summary

Title: Mastering Big Data Analytics with PySpark

Price: $59.99

Average Rating: 4.4

Number of Lectures: 41

Number of Quizzes: 9

Number of Published Lectures: 41

Number of Published Quizzes: 9

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a solid knowledge of vital Data Analytics concepts via practical use cases
Create elegant data visualizations using Jupyter
Run, process, and analyze large chunks of datasets using PySpark
Utilize Spark SQL to easily load big data into DataFrames
Create fast and scalable Machine Learning applications using MLlib with Spark
Perform exploratory Data Analysis in a scalable way
Achieve scalable, high-throughput and fault-tolerant processing of data streams using Spark Streaming

Who Should Attend

This course will greatly appeal to data science enthusiasts, data scientists, or anyone who is familiar with Machine Learning concepts and wants to scale out his/her work to work with big data.
If you find it difficult to analyze large datasets that keep growing, then this course is the perfect guide for you!

Target Audiences

This course will greatly appeal to data science enthusiasts, data scientists, or anyone who is familiar with Machine Learning concepts and wants to scale out his/her work to work with big data.
If you find it difficult to analyze large datasets that keep growing, then this course is the perfect guide for you!

PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark’s potential for performing effective analyses of large datasets. You’ll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you’ll delve into various Spark components and its architecture.

You’ll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You’ll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning.

By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization.

About the Author

Danny Meijer works as the Lead Data Engineer in the Netherlands for the Data and Analytics department of a leading sporting goods retailer. He is a Business Process Expert, big data scientist and additionally a data engineer, which gives him a unique mix of skills—the foremost of which is his business-first approach to data science and data engineering.

He has over 13-years’ IT experience across various domains and skills ranging from (big) data modeling, architecture, design, and development as well as project and process management; he also has extensive experience with process mining, data engineering on big data, and process improvement.

As a certified data scientist and big data professional, he knows his way around data and analytics, and is proficient in various types of programming language. He has extensive experience with various big data technologies and is fluent in everything: NoSQL, Hadoop, Python, and of course Spark.

Danny is a driven person, motivated by everything data and big-data. He loves math and machine learning and tackling difficult problems.

Course Curriculum

Chapter 1: Python and Spark: A Match Made in Heaven

Lecture 1: Course Overview

Lecture 2: Python versus Spark

Lecture 3: Preparing for the Course

Lecture 4: Connecting Jupyter to Spark

Chapter 2: Working with PySpark

Lecture 1: Getting to Know Spark

Lecture 2: The Power of Spark

Lecture 3: The Power of Spark MLlib

Lecture 4: Spark DataFrames

Lecture 5: Spark Data Operations

Chapter 3: Preparing Data Using Spark SQL

Lecture 1: Loading Data from CSV Files

Lecture 2: Fixing Issues in Our Data â€“ Part One

Lecture 3: Fixing Issues in Our Data â€“ Part Two

Lecture 4: Grouping, Joining, and Aggregating â€“ Part One

Lecture 5: Grouping, Joining, and Aggregating â€“ Part Two

Chapter 4: Machine Learning with Spark MLlib

Lecture 1: Machine Learning with Spark

Lecture 2: Building a Recommendation System with Spark MLlib â€“ Part One

Lecture 3: Building a Recommendation System with Spark MLlib â€“ Part Two

Lecture 4: Building a Recommendation System with Spark MLlib â€“ Part Three

Lecture 5: Finalizing our Recommendation System

Lecture 6: What We Have Learned So Far

Chapter 5: Classification and Regression

Lecture 1: Machine Learning with Spark

Lecture 2: Machine Learning Pipelines

Lecture 3: Running a Logistic Regression Pipeline

Lecture 4: Parameters, Features, and Persistence

Lecture 5: Frequent Pattern Mining and Statistics

Chapter 6: Analyzing Big Data

Lecture 1: Natural Language Processing with Spark

Lecture 2: Identifying Our Data

Lecture 3: Data Preparation and Exploration

Lecture 4: Creating Our Raw Training Data

Chapter 7: Processing Natural Language in Spark

Lecture 1: Data Preparation and Regular Expressions

Lecture 2: Data Cleaning and Transformation

Lecture 3: Training a Sentiment Analysis Model â€“ Part One

Lecture 4: Training a Sentiment Analysis Model â€“ Part Two

Chapter 8: Machine Learning in Real-Time

Lecture 1: Fetching Data from Twitter

Lecture 2: Spark Structured Streaming

Lecture 3: Managing and Converting Streams

Lecture 4: Assembling Our Streaming ML Solution

Lecture 5: A Structured Approach to ML Streaming

Chapter 9: The Power of PySpark

Lecture 1: Running Spark in Production

Lecture 2: Running Spark at Scale

Lecture 3: Tips, Tricks, and Take-Aways

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 6 votes
4 stars: 19 votes
5 stars: 29 votes

Frequently Asked Questions

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