Spark and Python for Big Data with PySpark is a 63-lesson 10 hours 35 minutes self-paced course by Udemy. Companies like Google, Facebook, Netflix, Airbnb, Amazon and NASA rely on Apache Spark to process data at a scale Hadoop MapReduce struggles with, running up to 100 times faster.
Course facts
Lessons
63
Duration
10 hours 35 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Udemy
Price
Premium
Companies like Google, Facebook, Netflix, Airbnb, Amazon and NASA rely on Apache Spark to process data at a scale Hadoop MapReduce struggles with, running up to 100 times faster. This course teaches Spark through Python, using the Spark 2.0 DataFrame syntax throughout.
What you'll practice
A Python crash course if you need a refresher, then straight into Spark DataFrames
Mock consulting projects that simulate real client problems, including customer churn classification with Logistic Regression and Random Forests
Building machine learning models with Spark's MLlib, including Gradient Boosted Trees
Spark SQL and Spark Streaming, including a spam filter with NLP and real-time tweet analysis
Infrastructure you'll touch
The course also covers the DataBricks platform, running Spark on Linux, and getting set up on AWS with EC2 and Elastic MapReduce for larger workloads.
Who it's for
Built for anyone with general programming skills, ideally in Python, who wants to add Big Data processing to their toolkit. Plan on roughly 20GB of local disk space, or a solid internet connection if you work in AWS instead.
Who teaches Spark and Python for Big Data with PySpark? Udemy
Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.
Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.
What lessons are included in Spark and Python for Big Data with PySpark?
This is a demo lesson (10:00 remaining)
You can watch up to 10 minutes for free. Subscribe to unlock all 63 lessons in this course and access 10,000+ hours of premium content across all courses.
Master statistics with Python through projects and quizzes. Learn with fun from industry experts. Ideal for careers in Data Analytics and Machine Learning.
Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a sea
Nest.js is an incredible backend framework that allows us to build scaleable Nodejs backends with very little complexity. A Microservice architecture is a popul
Brand new HTML & CSS course, just released in February 2023 Check out the promo video to see the beautiful, responsive projects we build in this course!
Watch the 100 Days of Code Python Pro Bootcamp free: 100 daily projects covering Python basics, web scraping, data science, automation and GUI apps.
58h 35m5/5
Frequently asked questions
What prior knowledge do I need before enrolling in this course?
Before enrolling in this course, you should have general programming skills. The course includes a Python crash course to get you familiar with Python syntax and programming concepts. However, having a basic understanding of programming logic and constructs will be beneficial to keep up with the pace of the lessons.
What kinds of projects will I work on during this course?
Throughout the course, you'll engage in various Mock Consulting Projects that simulate real-world scenarios. These projects include a DataFrame project exercise, Linear Regression consulting project, Logistic Regression consulting project, Random Forest classification consulting project, and a clustering consulting project, among others. These practical exercises are designed to help you apply Spark and PySpark tools to solve real data challenges.
Who is the target audience for this course?
This course is ideal for individuals interested in mastering big data technologies, specifically Apache Spark and Python. It's suitable for those looking to enhance their data analysis skills, such as data scientists, analysts, and IT professionals seeking to leverage Spark in their work. Individuals aiming to add Spark and PySpark to their resumes to increase their job marketability would also benefit from this course.
How does this course compare in depth to other big data courses?
This course offers a comprehensive look at Apache Spark, from setup to advanced data processing techniques. It covers the latest Spark 2.0 DataFrame syntax and includes sections on machine learning with MLlib, Spark SQL, and real-time data processing with Spark Streaming. Compared to other courses, it provides a wide-ranging exploration of Spark's capabilities, focusing on practical, hands-on projects to reinforce learning.
What specific tools and platforms are covered in the course?
The course covers several tools and platforms, including VirtualBox for local installation, AWS EC2 for cloud-based setups, Databricks for collaborative data science work, and AWS EMR for managed Hadoop frameworks. It also includes a section on setting up PySpark and using Jupyter Notebook for interactive data analysis.
Is there anything that is not covered in this course?
While the course extensively covers Apache Spark and its associated technologies, it does not delve into other big data frameworks like Hadoop MapReduce beyond a brief comparison with Spark. Additionally, it assumes prior general programming knowledge, so foundational programming concepts are not covered in depth outside of the Python crash course.
How much time should I expect to commit to complete this course?
The course comprises 63 lessons, including a variety of practical exercises and projects. While the exact runtime isn't specified, students should be prepared to dedicate a significant amount of time to both the instructional content and to engage thoroughly with the hands-on projects and exercises, which are crucial for mastering the skills taught in the course.
