To see the full workshop agenda, please visit the Schedule page.

Week 1: March 2, 2022 Beginner Tutorial

The overall goal of the beginner tutorial is to introduce topics of open data, reproducible science, and do basic data analysis and visualization with publicly available datasets, such as those found on the State Climate Office of North Carolina’s Cardinal data portal.

:computer: Beginner Tutorial (student version without answers): LINK

:computer: Beginner Tutorial (teacher version with answers): LINK

:movie_camera: Beginner Tutorial Recording: LINK

See the Resources page for more information about beginner tutorial-relevant resources.

Tutorial Goals

By the end of the Beginner Tutorial, participants will be able to:

  1. define open data and reproducible science

  2. describe how to navigate aspects of an R coding notebook

  3. recall how to extract public data from a web portal (Cardinal) and import it into a data software (R)

  4. demonstrate understanding of dataset and statistical software through exploratory data analysis plots and numerical summaries


Week 2: March 9, 2022 Advanced Tutorial

The overall goal of the advanced tutorial is to introduce topics of open data, reproducible science, practice accessing publicly available climate data on the cloud, such as those found on the National Centers for Environmental Information (NCEI) nClimGrid data, and carry out intermediate statistical analysis and modeling (e.g., time series modeling and clustering).

:computer: Advanced Tutorial (student version without answers): LINK

:computer: Advanced Tutorial (teacher version with answers): LINK

:movie_camera: Beginner Tutorial Recording: LINK

Tutorial Goals

By the end of the Advanced Tutorial, participants will be able to:

  1. define open data and reproducible science

  2. recognize the importance of statistical analysis for climate data analysis using cloud based data

  3. apply techniques to access and explore publicly available climate data from the cloud using exploratory data analysis

  4. create a machine learning model to predict results for a representative case study