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Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Database Selection.
This practical guide provides a step-by-step approach to database design. It is written in a non-technical style and includes numerous examples and illustrations.
Provides a comprehensive guide to Hadoop, a popular open-source framework for storing and processing large amounts of data. It covers the different components of Hadoop, as well as how to use Hadoop to perform data analysis.
Provides a comprehensive guide to Spark, a popular open-source framework for distributed computing. It covers the different components of Spark, as well as how to use Spark to perform data analysis.
Provides a comprehensive guide to machine learning with R, a popular programming language for data analysis. It covers the different machine learning algorithms, as well as how to use R to implement them.
Provides a comprehensive guide to deep learning with Python, a popular programming language for machine learning. It covers the different deep learning algorithms, as well as how to use Python to implement them.
Provides a comprehensive guide to Python for data analysis. It covers the different Python libraries for data analysis, as well as how to use Python to perform data analysis tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/5fayxe/database
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