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Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes
An introduction to managing and publishing research projects using Jupyter Notebooks, as well as to the possibilities and best practices for sharing and publishing researching using executable papers.
Software: Computer workstations with Jupyter Notebook are available for in-person tutorials in Bobst 617. For remote tutorials, while some patrons decide to approach tutorials as a demonstration of the software, other patrons approach tutorials with a more “hands-on” approach and wish to interact with the software during the tutorial. If the latter is the case, we recommend referencing our supported software page for additional information on accessing the software prior to the tutorial.
Duration: 60 min

Room description:

As our global pandemic continues, some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering

Prerequisites:
  • Comfort with reading and writing programming code in a text editor, though no particular experience in Python, R, or Julia is needed
  • Comfort with following instructions in using a command line to launch an application
Skills Taught / Learning Outcomes:
  • Learn about executable papers, how they are being used by researchers to share results, and how to publish research to the web
  • Explore the features of Jupyter Notebooks for writing code in Python, R, or Julia, and understand the best practices for structuring a notebook.
  • Learn how to publish a notebook to the web.
Class Materials:

Slides: https://nyu-dataservices.github.io/Intro-Jupyter-Notebooks

Related Classes:

Introduction to Python

Introduction to R

Managing a Personal Research Archive

Additional Training Materials:

Introduction to Jupyter notebooks via LinkedIn Learning (NYU NetID required)

Reproducible data analysis with jupyter notebooks [video]

DataCamp tutorial

Caltech Tutorial

Gallery of interesting jupyter notebooks

Feedback: bit.ly/feedbackds

Upcoming sessions for this tutorial