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 bi-monthly 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.
Starting in Fall 2024, Data Club will take on the format of “Day One” get togethers. What we mean by that is that the topic for the day is treated as though it is your first day ever seeing it. A brief bit of instruction on the topic will precede time given to Data Club members to experiment and play with their new knowledge, striving to imagine ways of incorporating the topic into their scholarly lives.
Data Club Meetings
- Data Club meets on an irregular schedule at Lehman 215. Please see the events list below to see upcoming Data Club meetings.
- To receive updates about Data Club, please follow us on Instagram, join our mailing list, and subscribe to our calendar.
- 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 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.
Data Club on GitHub
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 JavaScript Resources
In your journey into JavaScript, we suggest the following:
Additional Python Resources
In addition to the Python resources provided by Clio, we suggest the following:
- Python User Group (sponsored by Columbia's Foundations for Research Computing)
- 10 Minutes to Pandas
- Plotting and Programming in Python from Data Carpentry (This is a novice-level tutorial)
- Think Python 2e (free ebook)
Additional R Resources
In addition to the R resources provided by Clio, we suggest the following:
- Swirl
- R for Reproducible Scientific Analysis from Data Carpentry (This is a novice-level tutorial)
- R for Data Science
- edav.info v2, the R resources website for Professor Joyce Robbins’s “Exploratory Data Analysis and Visualization” course for Data Science Master’s students.
- Modern Plain Text Computing, a course taught by Kieran Healy at Duke for graduate students incorporating R into their scholarly workflow.
Additional Data Management Resources
Here are some additional data management resources:
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