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Data Engineering and Machine Learning using Spark

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Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others. In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering. The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case. NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.

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Enhance your data engineering career with this foundational course on Machine Learning using Apache Spark, offered by Coursera. Taught by industry experts, this self-paced course delves into practical applications of Spark MLlib, Spark Structured Streaming, and more. Over 480 hours, learners will gain hands-on experience in ETL tasks, regression, classification, and clustering, culminating in a final project. Ideal for IT and Computer Science professionals, the course requires prior knowledge of Apache Spark and Jupyter Notebooks. Subscription options include Starter and Professional tiers.