Track
Master how to process big data and leverage it efficiently with Apache Spark using the PySpark API.
Loved by learners at thousands of companies
Make progress on the go with our mobile courses and daily 5-minute coding challenges.
Course
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
Master PySpark to handle big data with ease—learn to process, query, and optimize massive datasets for powerful analytics!
Course
Learn the fundamentals of working with big data with PySpark.
Learn the fundamentals of working with big data with PySpark.
Course
Learn how to clean data with Apache Spark in Python.
Learn how to clean data with Apache Spark in Python.
Course
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Course
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Big Data with PySpark
Track
Complete
Lead Data Scientist, General Mills
No, prior knowledge of machine learning and Python is assumed if you start this track.
The programming language of this Track is Python.
Data analysts, data engineers, and machine learning engineers will benefit from this Track.
This Track will prepare you for your career by teaching you essential skills and techniques required to work with large datasets and build machine learning models using PySpark.
It usually takes 25 hours to complete this Track, but it can vary depending on the individual's pace.
A skill track is designed to focus on specific skills, while a career track is designed to provide a comprehensive learning experience for a specific job or career path.
The tasks included in this Track are Introduction to PySpark, Big Data Fundamentals with PySpark, Cleaning Data with PySpark, Feature Engineering with PySpark, Machine Learning with PySpark, and Building Recommendation Engines with PySpark.
The popular MovieLens dataset and the Million Songs dataset will be used in this Track for building a recommendation engine.
The tracks helped me complete my journey without feeling lost. Each course builds on the last, keeping me motivated and on track
DataCamp helped me transition from someone curious about data to someone actively applying these skills in my job
I've been using DataCamp for four years, and it's helped me transition from filling gaps in my skills to proactively creating value for my company
