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Data Validity

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Reading list

We've selected 15 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 Validity.
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Data Quality
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Provides a comprehensive overview of data quality, covering topics such as data quality dimensions, data quality assessment, and data quality improvement. It is an excellent resource for anyone who wants to learn more about the theory and practice of data quality. The author, Thomas Redman, well-known expert in the field of data quality.
Data Quality For The Information Age (Artech House...
Hardcover
$$$
Data Quality Management and Technology
Hardcover
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Data Quality: The Field Guide by Thomas Redman PhD...
Paperback Bunko
$$$$
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Data Science for Beginners
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Provides a comprehensive overview of data quality, covering topics such as data cleaning, data validation, and data governance. It is an excellent resource for anyone who wants to learn more about data quality and how to improve it.
Data Science for Beginners
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Data Science for Beginners
Kindle Edition
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Practical Guide to SAP HANA and Big Data Analytics
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Provides a practical guide to data validation for data science, covering topics such as data profiling, data cleansing, and data validation. It is an excellent resource for anyone who wants to learn more about how to validate data and improve its quality for data science.
Practical Guide to SAP HANA and Big Data Analytics
Paperback
$$
Practical Guide to SAP HANA and Big Data Analytics
Kindle Edition
$
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People and Data
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Provides a comprehensive overview of data quality, covering topics such as data quality dimensions, data quality assessment, and data quality improvement. It is an excellent resource for anyone who wants to learn more about the theory and practice of data quality. The author, Thomas Redman, well-known expert in the field of data quality.
People and Data
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People and Data
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Data Quality
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Provides a deep dive into data integrity, which includes data validation as a key aspect. It covers topics such as data cleansing, data standardization, and data deduplication.
Data Quality (Advances in Database Systems, 23)
Hardcover
$$$$
Data Quality (Advances in Database Systems Book 23)
Kindle Edition
$$$$
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Heterogeneous Graph Representation Learning and...
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Explores the challenges of data validation in the context of big data, where data volumes and complexity can make traditional data validation techniques infeasible.
Heterogeneous Graph Representation Learning and...
Hardcover
$$$$
(中文) Heterogeneous graph representation learning and...
Paperback
$$$
Heterogeneous Graph Representation Learning and...
Kindle Edition
$$$$
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An Introduction to Statistical Learning
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While this book does not exclusively focus on the topic of data validation, it is an excellent resource for understanding related concepts, such as data cleaning, feature engineering, and model selection. The authors are renowned statisticians and machine learning experts, and their expertise shines through in this comprehensive and accessible text.
An Introduction to Statistical Learning
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An Introduction to Statistical Learning
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The Data Warehouse ETL Toolkit
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Focuses on data validation within the context of data warehousing and ETL processes. It provides real-world examples and best practices for ensuring data integrity and consistency.
The Data Warehouse ETL Toolkit: Practical...
Paperback
$$
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Data Driven
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Focuses on how to handle data quality issues, including data validation. It provides practical techniques for handling missing data, dealing with data inconsistency, and improving data quality.
Data Driven
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Applied Text Analysis with Python
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While Python-specific, this book by a Python Core Developer and member of the Django Software Foundation provides an immensely readable and pragmatic approach to the implementation of data validation at scale in the context of web apps.
Applied Text Analysis with Python: Enabling...
Paperback
$$
Applied Text Analysis with Python: Enabling...
Kindle Edition
$$
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The Practitioner's Guide to Data Quality Improvement
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Provides a practical guide to data validation, covering topics such as data profiling, data cleansing, and data standardization. It is an excellent resource for anyone who wants to learn more about how to validate data and improve its quality.
The Practitioner's Guide to Data Quality Improvement
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The Practitioner's Guide to Data Quality Improvement
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Customer Experience Management for Water Utilities
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Provides a practical guide to data quality, covering topics such as data quality assessment, data quality improvement, and data quality management. It is an excellent resource for anyone who wants to learn more about how to improve the quality of data.
Customer Experience Management for Water Utilities:...
Paperback
$$$
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Successful Business Intelligence, Second Edition
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Provides a practical guide to data quality, covering topics such as data quality assessment, data quality improvement, and data quality management. It is an excellent resource for anyone who wants to learn more about how to improve the quality of data.
Successful Business Intelligence, Second Edition
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Successful Business Intelligence, Second Edition
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Practical Data Science with R, Second Edition
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While covering data validation briefly, this book by a former Google data scientist provides a comprehensive overview of data cleaning, wrangling, analysis, and visualization using R. It is particularly useful for beginners in the field.
Practical Data Science with R, Second Edition
Paperback
$$
Practical Data Science with R, Second Edition
Kindle Edition
$$
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High Performance SQL Server
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Provides a deep dive into data validation specifically within the context of SQL Server. It covers a wide range of topics, including data types, constraints, triggers, and stored procedures.
High Performance SQL Server: Consistent Response...
Paperback
$$
High Performance SQL Server: The Go Faster Book
Paperback
$$
High Performance SQL Server: Consistent Response...
Kindle Edition
$$
High Performance SQL Server: The Go Faster Book
Kindle Edition
$$
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