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Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)

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.What can you get from this course?
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
This course will provide learners with the skills and knowledge to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. Learners will gain an understanding of the fundamentals of data engineering and machine learning, including data processing, data analysis, data visualization, and more. They will learn how to use Spark to create and deploy ML models, and how to use Spark Structured Streaming to process streaming data. Additionally, learners will gain an understanding of the importance of data engineering and machine learning in the modern business world. By the end of the course, learners will have the skills and knowledge to apply their data engineering and machine learning skills to real-world problems.

How does this course contribute to professional growth?
Data Engineering and Machine Learning using Spark is a short course designed to help learners gain practical skills in working with Apache Spark for Data Engineering and Machine Learning (ML) applications. This course provides learners with the skills and knowledge to apply their data engineering and machine learning skills to real-world problems. Learners will gain an understanding of the fundamentals of data engineering and machine learning, including data processing, data analysis, data visualization, and more. They will also learn how to use Spark to create and deploy ML models, and how to use Spark Structured Streaming to process streaming data. Additionally, learners will gain an understanding of the importance of data engineering and machine learning in the modern business world. By the end of the course, learners will have the skills and knowledge to apply their data engineering and machine learning skills to real-world problems. This course contributes to professional growth by providing learners with the skills and knowledge to use data engineering and machine learning techniques to identify behaviors and preferences of prospects, clients, competitors, and others. Additionally, learners will be able to use Spark to create and deploy ML models, and use Spark Structured Streaming to process streaming data.

Is this course suitable for preparing further education?
Data Engineering and Machine Learning using Spark is a suitable course for preparing further education. It provides learners with foundational skills for working with Apache Spark and Jupyter Notebooks, as well as an understanding of the fundamentals of data engineering and machine learning. Learners will gain an understanding of the importance of data engineering and machine learning in the modern business world, and will be able to use Spark to create and deploy ML models, and use Spark Structured Streaming to process streaming data. Additionally, learners will be able to use data engineering and machine learning techniques to identify behaviors and preferences of prospects, clients, competitors, and others. This course is ideal for learners who are looking to gain practical skills in working with Apache Spark for Data Engineering and Machine Learning applications.