7 Best PySpark Courses and Certifications [FREE Course] 2025

Are you looking for the Best PySpark Courses and Certifications?… If yes, you are in the right place. I have listed the 7 Best PySpark Courses and Certifications from various platforms in this article.

Now, without any further ado, let’s get started-

Best PySpark Courses and Certifications

Table Of Content

1. Data Analysis Using Pyspark- Coursera
2. Spark- Udacity FREE Course
3. NoSQL, Big Data, and Spark Foundations Specialization- Coursera
4. Spark and Python for Big Data with PySpark- Udemy
5. PySpark & AWS: Master Big Data With PySpark and AWS- Udemy
6. Building Machine Learning Pipelines in PySpark MLlib- Coursera
7. Introduction to PySpark- DataCamp
FAQ

1. Data Analysis Using Pyspark– Coursera

Rating- 4.4/5

Time to Complete- 1.5 hours

This is a guided project, where you will work with the PySpark module in python. For this project, you will use an online music service website dataset.

The dataset has two CSV files listening.csv and genre.csv. Overall, this is not a very detailed course and covers the basics of PySpark. You will perform this project on the Google Colab platform.

Who Should Enroll?

Those who already know Python Programming.

Interested to Enroll?

If yes, then check out the course details here- Data Analysis Using Pyspark

2. Spark– Udacity FREE Course

Rating- NA

Time to Complete- 10 Hours

This is an entirely FREE course on Udacity. This course teaches you how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.

There are four parts to the course. First, you will learn about big data and the power of Spark. Next, you will learn how to perform data wrangling with Spark. You will also learn how to set up Spark Clusters with AWS and how to perform debugging and optimization.

At the end of this course, you will work on too many examples of different components of a typical machine learning pipeline including feature creation, model training, and hyper-parameter tuning.

Who Should Enroll?

Those who have previous Python and Data Analysis knowledge.

Interested to Enroll?

If yes, then check out the course details here- Spark

3. NoSQL, Big Data, and Spark Foundations Specialization– Coursera

Rating- 4.3/5

Time to Complete- 4 months (If you spend 3 hours/week)

This is a specialization program and has 3 courses. In the first course, you will learn about NoSQL, Big Data, Spark, and Hadoop, and understand how to work with Apache Spark for Data Engineering and Machine Learning applications.

In the next course, you will learn Apache Spark in detail, SparkSQL, Apache Spark User Interface, etc. The last course will cover Spark Structured Streaming, GraphFrames on Apache Spark, ETL Workloads, SparkML Fundamentals, Classification and Regression using Apache Spark, etc.

Who Should Enroll?

Those who are beginners.

Interested to Enroll?

If yes, then check out all details here- NoSQL, Big Data, and Spark Foundations Specialization

4. Spark and Python for Big Data with PySpark– Udemy

Rating- 4.5/5

Time to Complete- 10.5 hours

This course is listed as a “Bestseller” on Udemy. In this course, first, you will learn Python and Spark DataFrame basics. Next, you will learn Machine learning with MLlib, Linear Regression, Logistic Regression, Decision Trees, Random Forest, K-means clustering, etc.

This course will also cover Natural Language Processing concepts and Spark Streaming Twitter Project with Python.

Who Should Enroll?

Those who already have some Python experience.

Interested to Enroll?

If yes, then check out the course details here- Spark and Python for Big Data with PySpark

5. PySpark & AWS: Master Big Data With PySpark and AWS– Udemy

Rating- 4.6/5

Time to Complete- 19.5 hours

In this course, you will learn what is Big Data, Hadoop, PySpark, Spark Ecosystem, etc. The instructor also explains Spark RDDs, how to create Spark RDD, RDD Map (Lambda), etc.

Next, you will learn Spark DFs, how to create DF from RDD, Spark DF withColumnRenamed and Alias, etc. This course also explains Collaborative filtering, Spark Streaming with RDD, and ETL pipeline Flow.

Who Should Enroll?

Those who have prior knowledge of Python.

Interested to Enroll?

If yes, then check out the course details here- PySpark & AWS: Master Big Data With PySpark and AWS

6. Building Machine Learning Pipelines in PySpark MLlib– Coursera

Rating- 4.3/5

Time to Complete- 1.5 hours

In this guided project, you will get a good overview of the basic commands of PySpark. You will also understand how to clean the data and how to choose the best model from the pipeline by using cross-validation and parameter tuning.

But this is not a very detailed course to learn PySpark. This course is good if you want to get hands-on experience.

Who Should Enroll?

Those who know Python and Machine Learning basics.

Interested to Enroll?

If yes, then check out the course details here- Building Machine Learning Pipelines in PySpark MLlib

7. Introduction to PySpark– DataCamp

Rating- NA

Time to Complete- 4 hours

There are four chapters in this course. First, you will get an introduction to PySpark such as how to use Spark in Python, how to use DataFrames, etc.

In the next chapters, you will understand the pyspark.sql module and machine learning pipeline. At the end of this course, you will learn logistics regression, how to create the modeler, cross-validation, how to create the evaluator, etc.

Who Should Enroll?

Those who know Python Programming.

Interested to Enroll?

If yes, then check out the course details here- Introduction to PySpark

And here the list ends. I hope these 7 Best PySpark Courses and Certifications will help you to learn PySpark. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover the 7 Best PySpark Courses and Certifications. If you have any doubts or questions, feel free to ask me in the comment section.

And if you know of any of the Best PySpark Courses and Certifications, let me know in the comment section.

All the Best!

Enjoy Learning!

FAQ

Is PySpark easy to learn?

The ease of learning PySpark is subjective and can vary based on an individual’s prior experience with Python and distributed computing. For beginners, PySpark might initially present some challenges due to its distributed processing nature and the need to grasp concepts like RDDs, DataFrames, and transformations. However, with the availability of helpful resources, tutorials, and dedicated practice, one can gradually become proficient in PySpark.

How long does it take to learn PySpark?

It is difficult to provide an exact amount of time required to learn PySpark as it can vary significantly depending on individual factors and learning styles. However, on average, it may take approximately 2-3 months of consistent effort, including studying relevant resources, completing tutorials, and working on practical projects, to gain a solid understanding of PySpark.

Is PySpark easier than Python?

Comparing PySpark to Python, it’s important to note that PySpark is not inherently easier than Python itself. Python is widely regarded as a user-friendly language with a straightforward syntax, making it relatively easy for beginners to learn and understand. On the other hand, PySpark, while built on Python, introduces specialized concepts and complexities specific to big data processing. Understanding PySpark requires grasping concepts like RDDs, DataFrames, and transformations, which can initially pose challenges for newcomers. So, while Python may be considered easier to learn overall, PySpark requires additional effort and familiarity with distributed computing principles, making it relatively more complex.

Is PySpark SQL or Python?

PySpark is a combination of both Python and SQL. It is a library that integrates Python with Apache Spark, a distributed computing framework. With PySpark, you can write code using Python, which provides a familiar and flexible programming language. Additionally, PySpark offers a SQL interface called PySpark SQL, allowing you to utilize SQL queries and expressions for working with structured and semi-structured data. This means you have the flexibility to use Python’s programming features and the convenience of SQL-based operations when working with data in PySpark.

Is PySpark in demand?

Yes, PySpark is highly sought after in the industry due to the increasing demand for big data processing. Its integration with Python and scalability with Apache Spark make it a valuable tool for data professionals, leading to a strong demand for individuals skilled in PySpark for data engineering, analytics, and machine learning roles.

Do data engineers use PySpark?

Yes, data engineers commonly use PySpark for their data processing tasks. PySpark provides a scalable framework that allows data engineers to efficiently handle large datasets, perform data transformations, and build data pipelines for various data engineering tasks.

Which companies use PySpark?

PySpark is used by a range of companies including Netflix, Uber, Airbnb, Adobe, and NASA. These organizations leverage PySpark for processing and analyzing large datasets, powering recommendation systems, optimizing operations, improving user experiences, and conducting scientific research. Many other industries also rely on PySpark for their big data processing and analytics needs, making it a popular choice among organizations dealing with large-scale data.

Who should learn PySpark?

PySpark is valuable for data engineers, data scientists, data analysts, big data professionals, and researchers who deal with large-scale data processing and analysis. It equips them with the tools to handle massive datasets, build data pipelines, perform advanced analytics, and derive insights using distributed computing capabilities.

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’
– John Wooden

Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.

7 Best PySpark Courses and Certifications You Must Know in 2025

Best PySpark Courses and Certifications

1. Data Analysis Using Pyspark– Coursera

Who Should Enroll?

Interested to Enroll?

2. Spark– Udacity FREE Course

Who Should Enroll?

Interested to Enroll?

3. NoSQL, Big Data, and Spark Foundations Specialization– Coursera

Who Should Enroll?

Interested to Enroll?

4. Spark and Python for Big Data with PySpark– Udemy

Who Should Enroll?

Interested to Enroll?

5. PySpark & AWS: Master Big Data With PySpark and AWS– Udemy

Who Should Enroll?

Interested to Enroll?

6. Building Machine Learning Pipelines in PySpark MLlib– Coursera

Who Should Enroll?

Interested to Enroll?

7. Introduction to PySpark– DataCamp

Who Should Enroll?

Interested to Enroll?

Conclusion

FAQ

You May Also Be Interested In

Though of the Day…

Written By Aqsa Zafar

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