Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
PySpark Mastery: From Beginner to Advanced Data Processing
Rating: 3.8 out of 5(50 ratings)
10,273 students

PySpark Mastery: From Beginner to Advanced Data Processing

Unlock PySpark, covering Python basics, RDD programming, MySQL integration, machine learning, and advanced analytics
Created byEDUCBA Bridging the Gap
Last updated 3/2024
English

What you'll 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.

Explore related topics

Course content

3 sections • 41 lectures • 5h 41m total length

  • Introduction to PySpark9:10
  • Basics of Python10:41
  • Basics of Python Continue9:54

    Master Python basics for data processing, covering list operations (append, remove, insert, reverse, extend, pop, del), string methods (upper, lower, count, replace, strip), numpy arrays, and a PySpark intro.

  • Programming with RDD7:37
  • More Examples9:00
  • Foreach Loop8:05

    Create a spark context, parallelize values, and use foreach to print each value; apply a lambda filter for spark, then map key-value pairs and collect results.

  • Using Reduce Function7:37

    Master PySpark data processing by using reduce to sum arrays, and join and collect results with spark context and spark session. Learn caching with persist and is_cached to optimize performance.

  • Mysql Connectivity5:42
  • Viewing Records from Mysql10:49

    Explore viewing data from MySQL in spark using dataframes, print schema to inspect column types, then create dataframes from lists and load csv data with spark csv package for analysis.

  • More Examples Part 17:02

    Explore PySpark data processing by counting rows, listing train and test columns, computing describe statistics, selecting columns, and identifying distinct product_id values that differ between train and test.

  • More Examples Part 210:05

    Learn practical PySpark data processing skills with cross tab, pairwise frequencies for age and gender, handling duplicates and null values, filtering, group by, and data frame operations.

  • Pyspark Joins5:33
  • Pyspark Joins Examples8:48
  • More Examples on Mysql Part 113:00
  • More Examples on Mysql Part 24:46
  • Word Count12:00

Requirements

  • There are no specific prerequisites for this course, but basic knowledge of Python programming and familiarity with data analysis concepts would be beneficial.

Description

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.

Who this course is 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.
  • 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.

Report abuse