Data Club

Research Data Services Data Club

Data Club

Data Club, provided by Research Data Services, offers a series of strategic lessons and a supportive meeting space for members of the Columbia community of all levels to learn, share, and implement the philosophies, tools, and methodologies for a data-driven project of their choosing. In eight weekly meetings, the Data Club provides a short instructive session, free-work time, and a collaborative and exploratory environment for researchers to pursue a personal project with the assistance of Columbia University librarians and their data-minded peers.

If you are interested in staying up-to-date with Data Club, please join our mailing list.


Data Club Meetings

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  • Spring 2020 meetings for Data Club begin on February 27 at 4:30pm at Lehman 215. We will meet for 90 minutes every Thursday until April 23. Please see the Workshops Schedule for more information.
  • Data Club also welcomes members of the Columbia community who work with data and are interested to learn more about data analysis, management, and other aspects of the data life cycle at our biweekly meetings. 
  • Open to beginners and experts, Data Club meetings provide a space to experiment with new libraries or techniques.
  • Typically we will be diving deep with either Python or R.
  • Attendees should bring laptops.

Intro to Data (with Python or R)

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  • Data Club hosts a biweekly workshop, "Intro to Data (with Python or R)," aimed at complete beginners in data management, Python, and R.
  • Each workshop uses one language (R or Python) to introduce fundamental concepts around data management, including typing, filtering, mapping, and reducing.
  • Attendees should bring laptops, as the workshop begins with installing either R Studio or Anaconda.
  • Please see the Workshops Schedule for more information.

Data Club on GitHub

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We try to put all of our Data Club materials online in our two GitHub repositories. Clone them to keep up to date with our progress.

Additional Python Resources

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In addition to the Python resources provided by Clio, we suggest the following:

Additional R Resources

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In addition to the R resources provided by Clio, we suggest the following:

Additional Data Management Resources

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Here are some additional data management resources: