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Data Manager

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Salaries for Data Manager

City
Median
New York
$118,000
San Francisco
$127,000
Seattle
$156,000
See all salaries
City
Median
New York
$118,000
San Francisco
$127,000
Seattle
$156,000
Austin
$93,000
Toronto
$128,000
London
£95,000
Paris
€71,000
Berlin
€80,000
Tel Aviv
₪460,000
Singapore
S$135,000
Beijing
¥756,000
Shanghai
¥273,000
Shenzhen
¥474,000
Bengalaru
₹963,000
Delhi
₹524,000
Bars indicate relevance. All salaries presented are estimates. Completion of this course does not guarantee or imply job placement or career outcomes.

Path to Data Manager

Take the first step.
We've curated 24 courses to help you on your path to Data Manager. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

Reading list

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Provides a practical guide to data normalization, explaining the basics of data modeling and normalization. It also covers advanced topics such as denormalization and data warehousing.
Provides a comprehensive guide to data warehousing, covering topics such as data modeling, data integration, and data analysis. It is written by a team of experts from the Kimball Group, and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of adaptive design methods in clinical trials. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of Bayesian adaptive clinical trials. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of data modeling and database design, including normalization techniques. It good resource for students and professionals who want to learn more about data management.
Provides a practical guide to big data analytics, covering topics such as data mining, machine learning, and data visualization. It is written by a team of experts from IBM, and is suitable for both beginners and experienced practitioners.
Provides a comprehensive guide to Spark, covering topics such as data storage, data processing, and data analysis. It is written by a team of experts from Databricks, and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of the design and analysis of clinical trials. It includes a section on adaptive designs.
Provides a comprehensive overview of statistical methods in clinical trials. It includes a section on adaptive designs.
Provides a practical guide to data normalization, explaining the basics of data modeling and normalization. It also covers advanced topics such as denormalization and data warehousing.
Provides a comprehensive guide to Hadoop, covering topics such as data storage, data processing, and data analysis. It is written by a team of experts from Hortonworks, and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of clinical trials. It includes a section on adaptive designs.
Provides a comprehensive overview of data modeling and database design, including normalization techniques. It good resource for students and professionals who want to learn more about data management.
Classic text on data normalization, providing a theoretical foundation for the topic. It good resource for students and researchers who want to learn more about the theory behind normalization.
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