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
- 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
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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.