| 5⁄25 | Workshop Notes |
|---|---|
| Content | introduction to workshop tools: Slack (communication), git (source control) and Github (collaborative code), Python (coding) and Jupyter Notebooks (computational narratives), introduction to computation in the geosciences |
| Expected Outcomes |
• installation of git • creation of Github account • creation of Slack account • successful login to JupyterHub and “hello world” example • understanding of basic tools for computational geosciences |
| Readings & Supplements |
OPTIONAL › 2012. Downey, Allen; Think Python. → read/browse/skim ch.1 › (website) -- --; Slack Guides: Getting Started: https://get.slack.help/hc/en-us/categories/202622877-Slack-Guides. → Familiarize yourself with the Slack platform since we will be using it all summer. › 2018. Morra, Gabriele; The Future. → This is an interesting paper about where computational tools are likely to go in the Geosciences, including the role Python may play in that future. › (website) -- 2017; The Periodic Table of Data Science: https://www.datacamp.com/community/blog/data-science-periodic-table. → While broad in scope, this table is well worth becoming familiar with if you are interested it the multitude of data science tools available to use in your work. › (podcast.__init__: The Podcast About Python and the People Who Make It Great podcast) -- 2017; MetPy: Taming The Weather With Python - Episode 100: https://www.podcastinit.com/episode-100-metpy-with-ryan-may-sean-arms-and-john-leeman/. → This is a wonderful ~50 minute podcast about MetPy, a Python-based meterology package written and maintained right here at UCAR in Unidata. › (video) -- 2013 Setup: GitHub and Git Foundations: https://www.youtube.com/watch?v=7Inc0G0wutk&list=PL0lo9MOBetEHhfG9vJzVCTiDYcbhAiEqL&index=11. → This is an introduction to setting up git and the basics of GitHub. › (video) -- 2016 Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough: https://www.youtube.com/watch?v=HW29067qVWk. → This is a nice introductory video to Jupyter Notebooks. › (video) -- 2013 Webcast: The Basics of Git and GitHub Pt. 1 / July 2013: https://www.youtube.com/watch?v=U8GBXvdmHT4&t=0s&index=3&list=PL0lo9MOBetEGqyl2a4vyApFQi6t8PFCvQ. → This is a nice webcast recording (1 of 2) of more introductory git and GitHub concepts. › (video) -- 2013 Webcast: The Basics of Git and GitHub Pt. 2 / July 2013: https://www.youtube.com/watch?v=U8GBXvdmHT4&t=0s&index=4&list=PL0lo9MOBetEGqyl2a4vyApFQi6t8PFCvQ. → This is a continuation of the introductory webcast recording (1 of 2) for git and GitHub concepts. |
| Optional Homework | – |
| 6⁄1 | Workshop Notes |
|---|---|
| Content | hands on review of the Jupyter workflow for science; a quick introduction to JupyterLabs; Jupyter plugins of value; GitHub / git integrations; advanced strategies for solving problems with algorithms |
| Expected Outcomes |
• understand the workflow for Jupyter Notebooks • develop strategies for more complex problem solving and inquiry with Python and Jupyter • hands on problem solving in Jupyter building a mini-narrative |
| Readings & Supplements |
No assigned readings. Please complete readings from previous week if not current. |
| Optional Homework | – |
| 6⁄8 | Workshop Notes |
|---|---|
| Content | hands on introduction to some important Python libraries (some Geo-centric); writing better Python in scientific contexts, and understanding the supporting libraries to do it; hands on coding, plotting and explorations with a datasets |
| Expected Outcomes |
• understand and apply basic Python libraries for scientific work (graphing, plotting) • understand and apply Pythonic idioms in coding to save time and effort • working knowledge of building, documenting, plotting and saving a narrative/inquiry in Python |
| Readings & Supplements |
No assigned readings. Please complete readings from previous week if not current. |
| Optional Homework | – |
| 6⁄15 | Workshop Notes |
|---|---|
| Content | hands on introduction developing more advanced research workflows including notebook linking and sharing; publishing works in progress on GitHub (public and private); sharing finished work for informal publication including an introduction to platforms Zenodo and Figshare; developing the habit of data sharing and formal data citing |
| Expected Outcomes |
• understand and apply advanced research workflows in Jupyter • develop the habit of storing research inquiries on GitHub both publicly and privately in repositories • understand platforms for making published and unpublished work formally available on Zenodo, Figshare • understand the importance of formally citing dataset, notebooks and other important research inputs in the published and unpublished works |
| Readings & Supplements |
No assigned readings. Please complete readings from previous week if not current. |
| Optional Homework | – |