We may earn an affiliate commission when you visit our partners.

Data Librarian

Save

generative_async_error: 'str' object has no attribute 'get'

Read more

generative_async_error: 'str' object has no attribute 'get'

Share

Help others find this career page by sharing it with your friends and followers:

Salaries for Data Librarian

City
Median
New York
$105,000
San Francisco
$112,000
Seattle
$87,000
See all salaries
City
Median
New York
$105,000
San Francisco
$112,000
Seattle
$87,000
Austin
$104,000
Toronto
$97,000
London
£81,000
Paris
€70,000
Berlin
€72,000
Tel Aviv
₪461,000
Singapore
S$80,000
Beijing
¥422,000
Shanghai
¥80,000
Shenzhen
¥24,000
Bengalaru
₹455,000
Delhi
₹63,000
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 Librarian

Take the first step.
We've curated 15 courses to help you on your path to Data Librarian. 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

We haven't picked any books for this reading list yet.
Provides a practical guide to using public datasets for data science projects. It covers topics such as data cleaning, data analysis, and data visualization.
Provides a comprehensive guide to using Apache Spark for big data analytics. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use big data for analytics purposes.
Provides a comprehensive overview of data science. It covers topics such as data mining, machine learning, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding the principles of data science.
Provides a comprehensive guide to using MapReduce for data-intensive text processing. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use MapReduce for big data analysis purposes.
Provides a practical guide to statistics for data scientists. It covers topics such as data collection, data analysis, and data interpretation. While it does not focus specifically on public datasets, it provides a good foundation for understanding the statistical principles used in data science.
Provides a detailed guide to dimensional modeling, which key technique for organizing and managing data in a data warehouse.
Provides a comprehensive guide to using Pandas for data analysis. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use Pandas for data analysis purposes.
Provides a comprehensive guide to using R for data mining. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use R for data mining purposes.
Provides a practical introduction to data visualization. It covers topics such as data visualization techniques, data visualization tools, and data visualization best practices. While it does not focus specifically on public datasets, it provides a good foundation for understanding the principles of data visualization.
Provides a basic introduction to Microsoft SQL Server 2016, which relational database management system.
Provides a basic introduction to location intelligence, which is the use of data to understand the relationship between people, places, and things.
Provides a business-oriented introduction to data science. It covers topics such as data mining, machine learning, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use data for business purposes.
Provides a comprehensive guide to using Python for data analysis. It covers topics such as data loading, data cleaning, data analysis, and data visualization. While it does not focus specifically on public datasets, it provides a good foundation for understanding how to use Python for data analysis purposes.
Provides a comprehensive reference for MySQL, which relational database management system.
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser