Pyspark is a popular open-source big data processing framework that runs on the Apache Hadoop ecosystem. It provides an efficient and scalable way to process large datasets using distributed computing. As a result, there has been a growing demand for high-quality online courses that teach Pyspark to individuals and organizations looking to harness its full potential. In this article, we will explore some of the best Pyspark courses available online, highlighting their key features and benefits.
Here’s a look at the Best Pyspark Courses and Certifications Online and what they have to offer for you!
10 Best Pyspark Courses and Certifications Online
- 10 Best Pyspark Courses and Certifications Online
- 1. Spark and Python for Big Data with PySpark by Jose Portilla (Udemy) (Our Best Pick)
- 2. A Crash Course In PySpark by Kieran Keene (Udemy)
- 3. PySpark & AWS: Master Big Data With PySpark and AWS by AI Sciences, AI Sciences Team (Udemy)
- 4. Apache Spark 3 for Data Engineering & Analytics with Python by David Charles Academy (Udemy)
- 5. Complete PySpark Developer Course (Spark with Python) by Sibaram Kumar (Udemy)
- 6. PySpark Essentials for Data Scientists (Big Data + Python) by Layla AI (Udemy)
- 7. Data Analysis & Mining in Python & PySpark (2 Courses in 1) by Data Science Guide (Udemy)
- 8. Apache PySpark Fundamentals by Johnny F. (Udemy)
- 9. Learning PySpark by Packt Publishing (Udemy)
- 10. Hands-On PySpark for Big Data Analysis by Packt Publishing (Udemy)
1. Spark and Python for Big Data with PySpark by Jose Portilla (Udemy) (Our Best Pick)
The course titled “Spark and Python for Big Data with PySpark” is designed to teach students how to use one of the most popular programming languages, Python, with the latest big data technology, Spark. Spark is being used by top technology companies, such as Google, Facebook, Netflix, and Amazon, to analyze huge data sets. It is known to perform up to 100x faster than Hadoop MapReduce, and its demand has been increasing rapidly. The course will cover the basics of Python, Spark DataFrames, and how to use the MLlib Machine Library with the DataFrame syntax and Spark. It also includes exercises and mock consulting projects that simulate real-world situations.
The course covers the latest Spark Technologies, including Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees. Upon completion of the course, students will feel comfortable putting Spark and PySpark on their resume. The course also offers a full 30-day money-back guarantee and comes with a LinkedIn Certificate of Completion.
The course is divided into different sections, such as Introduction, Setting up Python with Spark, Databricks Setup, Local VirtualBox Set-up, AWS EC2 PySpark Set-up, AWS EMR Cluster Setup, Python Crash Course, Spark DataFrame Basics, Spark DataFrame Project Exercise, Introduction to Machine Learning with MLlib, Linear Regression, Logistic Regression, Decision Trees and Random Forests, K-means Clustering, Collaborative Filtering for Recommender Systems, Natural Language Processing, Spark Streaming with Python, and a Bonus section.
Overall, this course is designed to bring students up to speed with one of the best technologies for analyzing big data. With its focus on Spark and Python, it enables students to become one of the most knowledgeable individuals in the job market.
The “A Crash Course in PySpark” course, instructed by Kieran Keene, aims to teach the fundamentals of PySpark. Spark is currently one of the most in-demand Big Data processing frameworks.
The course covers the core concepts of PySpark, enabling learners to perform SQL or Python Pandas library tasks such as getting hold of data, handling missing data and cleaning data up, aggregating data, filtering data, pivoting data, and writing it back. All of these skills can be utilized to leverage Spark on large datasets and generate value from data.
The course includes an introduction, a scenario to get started, core concepts, a challenge, and a conclusion.
The PySpark & AWS: Master Big Data With PySpark and AWS course is designed to teach students how to use Spark, PySpark AWS, Spark applications, Spark EcoSystem, Hadoop and Mastering PySpark. The course starts with the basics and proceeds to advanced levels of data analysis, covering topics such as cleaning data, building features, implementing machine learning models, Spark RDDs, dataframes, and Spark SQL queries. Students will also explore the ecosystem of Spark and Hadoop and their underlying architecture, as well as how to use AWS cloud with Spark.
What sets this course apart is its learning by doing approach, where every theoretical explanation is followed by practical implementation. The course is easy to understand, comprehensive, and practical with live coding, providing students with state of the art and latest knowledge of this field. High-quality video content, in-depth course material, and informative handouts are also included, along with evaluation questions and detailed course notes.
The course consists of 140+ brief videos, with a total runtime of around 16 hours. Topics covered include Introduction, Hadoop, Spark EcoSystems, and Architectures, Spark RDDs, Spark DFs, Collaborative filtering, Spark Streaming, ETL Pipeline, and Project – Change Data Capture / Replication On Going. Students will have homework, tasks, activities, and quizzes to complete to evaluate and promote their learning, with most activities being coding-based.
PySpark is worth learning due to its increasing demand in Big Data processing and high salaries for Spark professionals. AWS is also worth learning as it is the fastest-growing public cloud. After completing this course, students will be able to relate the concepts and practicals of Spark and AWS with real-world problems, implement any project that requires PySpark knowledge from scratch, and know the theory and practical aspects of PySpark and AWS.
This course titled “Apache Spark 3 for Data Engineering & Analytics with Python” is designed to teach beginners to advanced learners how to use Python and PySpark 3.0.1 for data engineering and analytics using Databricks. The course is instructed by David Charles Academy and covers a variety of topics.
The course covers the following key objectives: learning the Spark architecture, execution concepts, transformations and actions using the Structured API, transformations and actions using the RDD API, setting up a local PySpark environment, interpreting the Spark Web UI and DAG, RDD API crash course, Spark DataFrame API, and more.
Some of the topics covered in the course include creating schemas and assigning data types, reading and writing data using the DataFrame reader and writer, detecting and dropping duplicates, combining two or more DataFrames, ordering DataFrames by specific columns, renaming and dropping columns, cleaning DataFrames, creating user-defined Spark functions, and aggregating DataFrames using Spark SQL functions.
Additionally, the course teaches learners how to create a Databricks account and cluster, use DML, DQL, and DDL with Spark SQL, learn Spark SQL functions, read CSV files from the Databricks file system, write complex SQL, create visualizations with Databricks, and answer various questions related to research data and sales analytics.
The course uses various technologies such as Python, Jupyter Notebook, Jupyter Lab, PySpark, Pandas, Matplotlib, Seaborn, and Databricks. Learners will also have the opportunity to work on a Python Spark project together and create a Sales Data Spark session.
The Complete PySpark Developer Course focuses on teaching PySpark in-depth to Data Engineers, Data Scientists, and others who want to process Big Data efficiently. The course covers various topics, including setting up a Hadoop Single Node Cluster and integrating it with Spark 2.x and 3.x, the installation of Standalone PySpark on Unix and Windows Operating Systems, Spark RDD fundamentals, Spark Cluster Architecture, Spark Shared Variables, Spark SQL Architecture, and DataFrame Built-in Functions.
The course also covers topics such as Partition, Repartition, Coalesce, and Extraction of various file types like CSV, Text, Parquet, Orc, Json, Avro, Hive, and JDBC. The course emphasizes the practical implementation of ETL using DataFrame Extraction APIs, Transformation APIs, and Loading APIs.
Additionally, the course covers optimization and management techniques, such as Join Strategies, Driver Conf, Parallelism Configurations, and Executor Conf. The course consists of several sections, including Introduction to Spark, Single Node Cluster Installation, HDFS Course, Python Crash Course, SparkSession, RDD Fundamentals, Spark Cluster Execution Architecture, Shared Variables, and Bonus Sections.
The course instructor, Sibaram Kumar, aims to help learners become complete PySpark Developers by providing them with practical examples and a real-time project implementation. Overall, the Complete PySpark Developer Course is a comprehensive guide for those interested in PySpark and Big Data processing.
The PySpark Essentials for Data Scientists (Big Data + Python) course is designed for data scientists looking for practical training in PySpark. The course covers using Python for Apache Spark, real-world datasets, and applicable coding knowledge for data science. The course includes over 100 lectures, quizzes, and example problems, as well as over 100,000 lines of code.
The course instructor, Layla AI, has extensive experience consulting as a data scientist for clients like the IRS, the US Department of Labor, and United States Veterans Affairs. Lecture and coding exercises are structured for real-world application, so students can understand how PySpark is used on the job. Custom functions for the MLlib API and MLflow are also covered.
Each section includes a concept review lecture, code-along activities, structured problem sets, and solutions. Real-world consulting projects with authentic datasets are provided in every section to help students apply what they have learned. Additionally, condensed review notebooks and handouts are provided for reference after completing the course.
The course covers several topics, including Dataframe essentials, Spark MLlib, Natural Language Processing in MLlib, Regression in MLlib, Clustering in PySpark, Frequent Pattern Mining in MLlib, and Spark Structured Streaming.
Overall, the PySpark Essentials for Data Scientists (Big Data + Python) course provides practical training in PySpark for data scientists looking to gain applicable coding knowledge, real-world datasets, and custom functions for the MLlib API and MLflow.
The course “Data Analysis & Mining in Python & PySpark” is a combination of two courses. Course 1 covers “Machine and Deep Learning in Python” and is designed to teach individuals how to create machine learning models and implement them in data mining. Participants will learn about the basic concepts of data mining, how to use data science libraries such as NumPy, Pandas, and Matplotlib to create machine learning models in Python, and how to build a deep learning model to solve a business problem. The course is structured to provide a simple and straightforward approach for building knowledge step by step until participants become familiar with the most commonly used machine learning algorithms.
Course 2 is titled “Data Analysis in PySpark” and covers Apache Spark, a powerful tool used in big data analysis. Participants will learn what Spark is, how it runs, and how data is stored in Spark. They will also learn how to configure the Python programming environment to run Spark code, conduct data analysis using real big data, import and clean data, and conduct business analysis using several Spark functions. Additionally, participants will learn how to create SQL queries inside PySpark to run data analysis.
The course also includes a third section that provides a simple review of statistical principles that are necessary for data analysis. The course consists of three sections: Introduction to Data Mining & Machine Learning in Python, Introduction to Learn Data Analysis in PySpark, and Statistics for Data Analysis. Participants will learn how to set up the programming environment, use supervised and unsupervised learning algorithms, and how to perform data analysis in PySpark. They will also learn the basics of statistics needed for data analysis.
Overall, the course is designed to provide individuals with a comprehensive understanding of data analysis and mining, machine learning, and deep learning in Python & PySpark. The course is structured to be accessible to individuals without any previous coding knowledge, and participants will learn to type codes in Python from scratch.
The Apache PySpark Fundamentals course is a comprehensive course that teaches the fundamentals of Apache Spark with Python. It is designed to provide learners with the necessary knowledge to develop Spark applications using PySpark, the Python API for Spark. By the end of the course, learners will have gained in-depth knowledge about Apache Spark and general big data analysis.
The course aims to help learners get comfortable with PySpark, explaining what it has to offer and how it can enhance data science work. The course starts by introducing the Spark ecosystem, detailing its advantages over other data science platforms, APIs, and toolsets. It then delves into the DataFrame API and how it helps solve many big data challenges. The course also covers Resilient Distributed Datasets (RDDs), which are the building blocks of Spark.
The course is structured into six sections, including an introduction and conclusion. The course content covers a range of topics, including an introduction to Apache Spark, DataFrames, Functions, and Resilient Distributed Datasets.
Overall, the Apache PySpark Fundamentals course is a valuable resource for anyone looking to learn about Apache Spark and big data analysis. It is suitable for developers, data scientists, and anyone interested in working with big data.
The “Learning PySpark” course offered by Packt Publishing introduces students to using Python and Apache Spark for building and deploying data-intensive applications at scale. The course begins with an overview of Spark and its architecture, followed by instruction on setting up a Python environment for Spark. Techniques for collecting and processing data are covered, along with a review of RDDs and DataFrames. The course also includes instruction on using SQL to interact with DataFrames, and concludes with mastery of data collection techniques through distributed data processing.
The author of the course, Tomasz Drabas, is a Data Scientist with over 12 years’ international experience in data analytics and data science. He has worked in advanced technology, airlines, telecommunications, finance, and consulting, and has published scientific papers, attended international conferences, and served as a reviewer for scientific journals.
The “Learning PySpark” course is divided into sections covering a brief primer on PySpark, Resilient Distributed Datasets and Actions, DataFrames and Transformations, and Data Processing with Spark DataFrames. Techniques for reading data from files and HDFS, specifying schemas, and utilizing lazy execution are also addressed in the course. By the end of the course, students will have gained a solid understanding of Spark and Python, and be able to process data effectively using Spark DataFrames.
This course, titled “Hands-On PySpark for Big Data Analysis”, is offered by Packt Publishing and aims to teach learners how to use PySpark for analytics on large data sets. The course promises to provide practical, hands-on experience in using Spark and its Python API to create performant analytics on large-scale data. The course is designed to help learners go from working on prototypes on their local machines to handling messy data in production and at scale.
The course is divided into six sections, namely: installing PySpark and setting up the development environment, getting big data into the Spark environment using RDDs, cleaning and wrangling big data with Spark Notebooks, aggregating and summarizing data into useful reports, powerful exploratory data analysis with MLlib, and putting structure on big data with SparkSQL. The course aims to teach learners how to create robust and responsible applications on big data without having to reinvent the wheel.
The course is authored by Colibri Digital, a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company provides expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. The course instructor, Rudy Lai, is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. He has also worked with HighDimension.IO, a machine learning consultancy, and has over five years of experience in quantitative trading at leading investment banks such as Morgan Stanley.
The course promises to provide learners with practical, hands-on experience in using PySpark for analytics on large data sets. The course is designed to help learners go from working on prototypes on their local machines to handling messy data in production and at scale. The course is authored by Colibri Digital and instructed by Rudy Lai, who has extensive experience in data science and machine learning. The course is divided into six sections, each addressing a key aspect of PySpark for big data analysis.