PySpark Mastery: From Beginner to Advanced Data Processing

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EDUCBA Bridging the Gap

5:41:39

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1. Pyspark Beginner

1. Introduction to PySpark.mp4

09:10

2. Basics of Python.mp4

10:41

3. Basics of Python Continue.mp4

09:54

4. Programming with RDD.mp4

07:37

5. More Examples.mp4

09:00

6. Foreach Loop.mp4

08:05

7. Using Reduce Function.mp4

07:37

8. Mysql Connectivity.mp4

05:42

9. Viewing Records from Mysql.mp4

10:49

10. More Examples Part 1.mp4

07:02

11. More Examples Part 2.mp4

10:05

12. Pyspark Joins.mp4

05:33

13. Pyspark Joins Examples.mp4

08:48

14. More Examples on Mysql Part 1.mp4

13:00

15. More Examples on Mysql Part 2.mp4

04:46

16. Word Count.mp4

12:00

2. Pyspark Intermediate

1. Introduction to Pyspark Intermediate.mp4

01:18

2. Liner Regation.mp4

09:02

3. Output Column.mp4

06:41

4. Test Data.mp4

06:43

5. Prediction.mp4

07:21

6. Generalized Linear Regression.mp4

12:00

7. Forest Rogation.mp4

12:16

8. Binomial Logistic Regression Part 1.mp4

09:01

9. Binomial Logistic Regression Part 2.mp4

06:46

10. Binomial Logistic Regression Part 3.mp4

08:44

11. Binomial Logistic Regression Part 4.mp4

10:39

12. Multinomial Logistic Regression.mp4

09:28

13. Multinomial Logistic Regression Continue.mp4

06:30

14. Decision Tree.mp4

06:57

15. Random Forest.mp4

06:53

16. K-Means Model.mp4

08:55

3. Pyspark Advanced

1. Introduction to Pyspark Advance.mp4

01:34

2. RFM Analysis.mp4

10:21

3. RFM Analysis Continue.mp4

12:12

4. K-Means Clustering.mp4

09:17

5. K-Means Clustering Continue.mp4

10:26

6. Image to Text.mp4

07:23

7. PDF to Text.mp4

06:56

8. Monte Carlo Simulation Part 1.mp4

05:41

9. Monte Carlo Simulation Part 2.mp4

08:46

Description

Unlock PySpark, covering Python basics, RDD programming, MySQL integration, machine learning, and advanced analytics

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.

Who is this 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.

What You Need to Know?

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

More details

Description
Welcome to the PySpark Mastery Course â€“ 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.

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