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PySpark Mastery: From Beginner to Advanced Data Processing

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PySpark Mastery: From Beginner to Advanced Data Processing, available at $19.99, has an average rating of 3.75, with 41 lectures, based on 30 reviews, and has 10194 subscribers.

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You will learn about Master the basics of PySpark, including RDD programming and Python essentials. Gain hands-on experience in integrating PySpark with MySQL for seamless data processing. Explore intermediate topics like linear regression, generalized linear regression, and forest regression for predictive modeling. Dive into advanced PySpark concepts, including RFM analysis, K-Means clustering, image-to-text conversion, PDF-to-text extraction, and Monte Carlo simulation. Develop practical skills in PySpark to manipulate, analyze, and visualize data for real-world applications. This course is ideal for individuals who are The target audience for these PySpark Tutorials includes ones such as the developers, analysts, software programmers, consultants, data engineers, data scientists , data analysts, software engineers, Big data programmers, Hadoop developers. Other audience includes ones such as students and entrepreneurs who are looking to create something of their own in the space of big data. or This course is designed for aspiring data professionals, analysts, and developers looking to enhance their skills in PySpark for big data processing. It is suitable for individuals with a foundational understanding of Python and an interest in advanced data analytics. It is particularly useful for The target audience for these PySpark Tutorials includes ones such as the developers, analysts, software programmers, consultants, data engineers, data scientists , data analysts, software engineers, Big data programmers, Hadoop developers. Other audience includes ones such as students and entrepreneurs who are looking to create something of their own in the space of big data. or This course is designed for aspiring data professionals, analysts, and developers looking to enhance their skills in PySpark for big data processing. It is suitable for individuals with a foundational understanding of Python and an interest in advanced data analytics.

Enroll now: PySpark Mastery: From Beginner to Advanced Data Processing

Summary

Title: PySpark Mastery: From Beginner to Advanced Data Processing

Price: $19.99

Average Rating: 3.75

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master the basics of PySpark, including RDD programming and Python essentials.
  • Gain hands-on experience in integrating PySpark with MySQL for seamless data processing.
  • Explore intermediate topics like linear regression, generalized linear regression, and forest regression for predictive modeling.
  • Dive into advanced PySpark concepts, including RFM analysis, K-Means clustering, image-to-text conversion, PDF-to-text extraction, and Monte Carlo simulation.
  • Develop practical skills in PySpark to manipulate, analyze, and visualize data for real-world applications.

Who Should Attend

  • The target audience for these PySpark Tutorials includes ones such as the developers, analysts, software programmers, consultants, data engineers, data scientists , data analysts, software engineers, Big data programmers, Hadoop developers. Other audience includes ones such as students and entrepreneurs who are looking to create something of their own in the space of big data.
  • This course is designed for aspiring data professionals, analysts, and developers looking to enhance their skills in PySpark for big data processing. It is suitable for individuals with a foundational understanding of Python and an interest in advanced data analytics.

Target Audiences

  • The target audience for these PySpark Tutorials includes ones such as the developers, analysts, software programmers, consultants, data engineers, data scientists , data analysts, software engineers, Big data programmers, Hadoop developers. Other audience includes ones such as students and entrepreneurs who are looking to create something of their own in the space of big data.
  • This course is designed for aspiring data professionals, analysts, and developers looking to enhance their skills in PySpark for big data processing. It is suitable for individuals with a foundational understanding of Python and an interest in advanced data analytics.

Welcome to the PySpark Mastery Course – a comprehensive journey from beginner to advanced levels in the powerful world of PySpark. Whether you are new to data processing or seeking to enhance your skills, this course is designed to equip you with the knowledge and hands-on experience needed to navigate PySpark proficiently.

Section 1: PySpark Beginner

This section serves as the foundation for your PySpark journey. You’ll start with an introduction to PySpark, understanding its significance in the world of data processing. To ensure a solid base, we delve into the basics of Python, emphasizing key concepts that are crucial for PySpark proficiency. The section progresses with hands-on programming using Resilient Distributed Datasets (RDDs), practical examples, and integration with MySQL databases. As you complete this section, you’ll possess a fundamental understanding of PySpark’s core concepts and practical applications.

Section 2: PySpark Intermediate

Building on the basics, the intermediate section introduces you to more advanced concepts and techniques in PySpark. You’ll explore linear regression, output column customization, and delve into real-world applications with predictive modeling. Specific focus is given to topics such as generalized linear regression, forest regression, and logistic regression. By the end of this section, you’ll be adept at using PySpark for more complex data processing and analysis tasks.

Section 3: PySpark Advanced

In the advanced section, we push the boundaries of your PySpark capabilities. You’ll engage in advanced data analysis techniques, such as RFM analysis and K-Means clustering. The section also covers innovative applications like converting images to text and extracting text from PDFs. Furthermore, you’ll gain insights into Monte Carlo simulation, a powerful tool for probabilistic modeling. This section equips you with the expertise needed to tackle intricate data challenges and showcases the versatility of PySpark in real-world scenarios.

Throughout each section, practical examples, coding exercises, and real-world applications will reinforce your learning, ensuring that you not only understand the theoretical concepts but can apply them effectively in a professional setting. Whether you’re a data enthusiast, analyst, or aspiring data scientist, this course provides a comprehensive journey through PySpark’s capabilities.

Course Curriculum

Chapter 1: Pyspark Beginner

Lecture 1: Introduction to PySpark

Lecture 2: Basics of Python

Lecture 3: Basics of Python Continue

Lecture 4: Programming with RDD

Lecture 5: More Examples

Lecture 6: Foreach Loop

Lecture 7: Using Reduce Function

Lecture 8: Mysql Connectivity

Lecture 9: Viewing Records from Mysql

Lecture 10: More Examples Part 1

Lecture 11: More Examples Part 2

Lecture 12: Pyspark Joins

Lecture 13: Pyspark Joins Examples

Lecture 14: More Examples on Mysql Part 1

Lecture 15: More Examples on Mysql Part 2

Lecture 16: Word Count

Chapter 2: Pyspark Intermediate

Lecture 1: Introduction to Pyspark Intermediate

Lecture 2: Liner Regation

Lecture 3: Output Column

Lecture 4: Test Data

Lecture 5: Prediction

Lecture 6: Generalized Linear Regression

Lecture 7: Forest Rogation

Lecture 8: Binomial Logistic Regression Part 1

Lecture 9: Binomial Logistic Regression Part 2

Lecture 10: Binomial Logistic Regression Part 3

Lecture 11: Binomial Logistic Regression Part 4

Lecture 12: Multinomial Logistic Regression

Lecture 13: Multinomial Logistic Regression Continue

Lecture 14: Decision Tree

Lecture 15: Random Forest

Lecture 16: K-Means Model

Chapter 3: Pyspark Advanced

Lecture 1: Introduction to Pyspark Advance

Lecture 2: RFM Analysis

Lecture 3: RFM Analysis Continue

Lecture 4: K-Means Clustering

Lecture 5: K-Means Clustering Continue

Lecture 6: Image to Text

Lecture 7: PDF to Text

Lecture 8: Monte Carlo Simulation Part 1

Lecture 9: Monte Carlo Simulation Part 2

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 2 votes
  • 3 stars: 2 votes
  • 4 stars: 9 votes
  • 5 stars: 15 votes

Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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