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zejr4t|
Find a path to becoming a Data Sourcing. Learn more at:
OpenCourser.com/topic/zejr4t/data
Reading list
We've selected eight 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
Data Sourcing.
Provides a comprehensive overview of data sourcing for data mining. It covers best practices for data acquisition, data preprocessing, and feature engineering for data mining. It is essential reading for anyone involved in data mining or data science.
Provides a comprehensive overview of data sourcing for machine learning. It covers best practices for data acquisition, data preprocessing, and feature engineering. It is essential reading for anyone involved in machine learning or data science.
Provides a comprehensive overview of data sourcing for big data. It covers best practices for data acquisition, data storage, and data processing. It is essential reading for anyone involved in big data or data science.
Provides a comprehensive overview of data sourcing for data warehousing. It covers best practices for data acquisition, data integration, and data storage. It is essential reading for anyone involved in data warehousing or data science.
Provides a comprehensive overview of data sourcing for data visualization. It covers best practices for data acquisition, data cleaning, and data preparation for data visualization. It is essential reading for anyone involved in data visualization or data science.
Provides a comprehensive overview of data sourcing strategies. It covers best practices for data acquisition, data quality, and data governance. It is essential reading for anyone involved in data sourcing or data management.
Provides a comprehensive overview of data sourcing for business intelligence, covering data extraction, integration, and analysis techniques. It is particularly relevant for those interested in using data to drive business decisions.
Provides a comprehensive overview of data sourcing for predictive analytics. It covers best practices for data acquisition, data preprocessing, and feature engineering. It is essential reading for anyone involved in predictive analytics or data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/zejr4t/data
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