{"id":9568,"date":"2026-08-10T10:08:53","date_gmt":"2026-08-10T10:08:53","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-spark-ray-and-python-for-scalable-data-science-2021-6\/"},"modified":"2026-08-10T10:08:53","modified_gmt":"2026-08-10T10:08:53","slug":"oreilly-spark-ray-and-python-for-scalable-data-science-2021-6","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-spark-ray-and-python-for-scalable-data-science-2021-6\/","title":{"rendered":"Oreilly \u2013 Spark, Ray, and Python for Scalable Data Science 2021-6"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Spark, Ray, and Python for Scalable Data Science. This course teaches you how to implement data science and machine learning projects at scale using Spark, Ray, and Python. Given the growing need for big data analytics, this course provides basic concepts and hands-on exercises to master distributed computing tools. First, you will be introduced to data science workflows and the necessity of using frameworks like Spark and Ray. Then, the basics of distributed systems and how these tools work are examined. Next, Spark is taught in a practical way with examples such as the RDD data structure and MapReduce operations. It also shows the application of Spark in natural language processing (NLP) and exploratory data analysis (EDA) at scale. In the section on Ray, its differences and similarities with Spark are compared and methods for distributing functions and objects using Ray are explained. Finally, the application of Ray to scaling machine learning model training, hyperparameter tuning, and deploying models in an operational environment is explored. Combining theory and practice, this course provides the skills needed to manage large-scale data projects.<\/span><\/p>\n

What you will learn:<\/span><\/h3>\n