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We need to create outlines/templates for Tutorials by level, so that new people writing a tutorial have some place to start and we have a checklist to make sure it covers the obvious.
Action Items
paste the tutorials into chatGPT by level, and ask it to outline their commonalities and create markdown templates
Key Differences Between Beginner vs. Intermediate Tutorials
Scope & Depth
Beginner tutorials focus on single, isolated skills (e.g., loading CSVs, making simple plots, cleaning data). They are usually short, linear, and prescriptive (“do this, then that”).
Intermediate tutorials (like Web Scraping and Geospatial Analysis) combine multiple skills into a workflow — they don’t just show one tool, they show how tools interconnect (requests → parsing → storage, or geopandas → shapely → visualization).
Format & Presentation
Beginners: Consistent structure → intro, short demo, small code snippets, a few links.
Intermediates: Longer, more narrative style with sections (“Overview,” “Tools,” “Putting it all together”) and often HTML formatting, tables, or embedded images. They also cite external references and real Hack for LA projects.
Audience Assumptions
Beginner tutorials assume the learner is new to data science and Python, so they explicitly define terms and provide guided steps.
Intermediate tutorials assume you already know Python/pandas basics and can navigate documentation; they focus on domain-specific knowledge (e.g., CRSs, OSMnx, pysal).
Outcomes & Use Cases
Beginner tutorials → “You will learn how to clean a dataset” (skill-focused).
Intermediate tutorials → “You will learn how to combine multiple datasets, resolve geospatial issues, and produce useful civic tech maps” (project-focused).
Maintenance Needs
Beginners are relatively static — code snippets don’t change often.
Intermediates link to external repos, APIs, and evolving packages — they require ongoing maintenance (e.g., OSMnx or pysal updates).
Why This Matters for Your Checklists / Documentation
Beginner checklists should emphasize completion of small exercises (“Did the learner install pandas? Did they plot a histogram?”).
Intermediate checklists should emphasize workflow understanding and application (“Did the learner successfully join two datasets? Did they use CRS correctly? Did they map results with folium?”).
So, the intermediate ones need more conceptual checkpoints (grasp of why, not just how).
Core Concepts: Explain the intermediate theory/frameworks behind the tutorial, not just “how” but also “why.”
Step-by-Step Walkthrough: A guided example or pipeline that ties the core concepts together.
Hands-On Exercises: Independent practice tasks, including stretch goals or variations on the walkthrough.
Common Pitfalls & Troubleshooting: Document frequent errors, debugging strategies, or misconceptions.
Best Practices: Efficiency tricks, sustainability practices, or standards relevant to the workflow.
Reflection & Discussion Prompts: Encourage learners to connect tutorial lessons to real-world civic tech or professional applications.
Next Steps / Advanced Extensions: Point to advanced tutorials, related skills, or scaling challenges.
Glossary / Key Terms: Define specialized terminology introduced in the tutorial.
Additional Resources: Curated links to docs, videos, and advanced reading.
Contributors: Credit authors, reviewers, and maintainers.
Issues Referenced: Link back to GitHub issues (if tutorial originated from repo discussions).
Template View
Intermediate Tutorial Checklist Template
Title & Introduction
Concise title describing the skill/technique.
Short overview of why the topic matters in practice.
Real-world or civic tech examples that make it relevant.
Learning Objectives
Clear, action-oriented goals (e.g., “By the end of this tutorial, you will be able to…”).
Typically 3–5 bullet points.
Prerequisites
Skills: what learners should already know (e.g., Python basics, Git).
Tools: required installations/accounts (e.g., Jupyter, Google Drive, GitHub).
Setup Instructions
Step-by-step environment setup (repos, libraries, data files).
Screenshots or code snippets where helpful.
Links to official docs for installation.
Core Concepts
Explain intermediate-level concepts (not just “how,” but “why”).
Provide diagrams, workflows, or conceptual frameworks where useful.
Step-by-Step Walkthrough
A guided, reproducible example that ties the concepts together.
Code snippets, terminal commands, or GUI steps as needed.
Incremental build-up: small wins before the full pipeline/project.
Hands-On Exercise
Task for the learner to try independently (variation on the walkthrough).
Include dataset or problem statement.
Stretch goal(s) for advanced learners.
Common Pitfalls & Troubleshooting
Document frequent errors or misconceptions.
Provide quick fixes or debugging tips.
Discussion & Reflection
Prompts to connect the exercise to real-world applications.
Example: “How would you adapt this workflow for a different dataset?”
Next Steps / Advanced Topics
Suggest follow-up tutorials, advanced readings, or extensions.
Could include integration with other tools, scaling techniques, or optimization.
Glossary / Key Terms
Define domain-specific terminology introduced in the tutorial.
References & Resources
Links to external tutorials, academic references, or tool documentation.
Links to related Hack for LA projects, repos, or dashboards.
Contributors
Credit authors and maintainers.
Optional: invite learners to contribute back (PRs, examples, feedback).
Template Copy
### Intermediate Tutorial Checklist Template
- [ ] **Title & Introduction**
- Concise title describing the skill/technique.
- Short overview of why the topic matters in practice.
- Real-world or civic tech examples that make it relevant.
- [ ] **Learning Objectives**
- Clear, action-oriented goals (e.g., “By the end of this tutorial, you will be able to…”).
- Typically 3–5 bullet points.
- [ ] **Prerequisites**
- Skills: what learners should already know (e.g., Python basics, Git).
- Tools: required installations/accounts (e.g., Jupyter, Google Drive, GitHub).
- [ ] **Setup Instructions**
- Step-by-step environment setup (repos, libraries, data files).
- Screenshots or code snippets where helpful.
- Links to official docs for installation.
- [ ] **Core Concepts**
- Explain intermediate-level concepts (not just “how,” but “why”).
- Provide diagrams, workflows, or conceptual frameworks where useful.
- [ ] **Step-by-Step Walkthrough**
- A guided, reproducible example that ties the concepts together.
- Code snippets, terminal commands, or GUI steps as needed.
- Incremental build-up: small wins before the full pipeline/project.
- [ ] **Hands-On Exercise**
- Task for the learner to try independently (variation on the walkthrough).
- Include dataset or problem statement.
- Stretch goal(s) for advanced learners.
- [ ] **Common Pitfalls & Troubleshooting**
- Document frequent errors or misconceptions.
- Provide quick fixes or debugging tips.
- [ ] **Discussion & Reflection**
- Prompts to connect the exercise to real-world applications.
- Example: “How would you adapt this workflow for a different dataset?”
- [ ] **Next Steps / Advanced Topics**
- Suggest follow-up tutorials, advanced readings, or extensions.
- Could include integration with other tools, scaling techniques, or optimization.
- [ ] **Glossary / Key Terms**
- Define domain-specific terminology introduced in the tutorial.
- [ ] **References & Resources**
- Links to external tutorials, academic references, or tool documentation.
- Links to related Hack for LA projects, repos, or dashboards.
- [ ] **Contributors**
- Credit authors and maintainers.
- Optional: invite learners to contribute back (PRs, examples, feedback).
Activity
ExperimentsInHonesty commentedon Aug 18, 2025
Generic Markdown Outline for Beginner Tutorials
✅ Tutorial Creation Checklist
Template view
[Insert Tutorial Title Here]
Overview
[Insert content here]
Purpose
[Insert content here]
Prerequisites
[Insert content here]
Setup Before Workshop
[Insert content here]
Core Concepts
Concept 1: [Insert topic here]
[Insert content here]
Concept 2: [Insert topic here]
[Insert content here]
Concept 3: [Insert topic here]
[Insert content here]
Hands-On Lessons & Exercises
Best Practices & Tips
[Insert content here]
Examples
[Insert content here]
Additional Resources
[Insert content here]
Contributors
[Insert content here]
Issues Referenced
[Insert content here]
Template copy
[-]Tutorial Template creation - Beginner[/-][+]Tutorial Template creation[/+]ExperimentsInHonesty commentedon Sep 2, 2025
Key Differences Between Beginner vs. Intermediate Tutorials
Beginner tutorials focus on single, isolated skills (e.g., loading CSVs, making simple plots, cleaning data). They are usually short, linear, and prescriptive (“do this, then that”).
Intermediate tutorials (like Web Scraping and Geospatial Analysis) combine multiple skills into a workflow — they don’t just show one tool, they show how tools interconnect (requests → parsing → storage, or geopandas → shapely → visualization).
Beginners: Consistent structure → intro, short demo, small code snippets, a few links.
Intermediates: Longer, more narrative style with sections (“Overview,” “Tools,” “Putting it all together”) and often HTML formatting, tables, or embedded images. They also cite external references and real Hack for LA projects.
Beginner tutorials assume the learner is new to data science and Python, so they explicitly define terms and provide guided steps.
Intermediate tutorials assume you already know Python/pandas basics and can navigate documentation; they focus on domain-specific knowledge (e.g., CRSs, OSMnx, pysal).
Beginner tutorials → “You will learn how to clean a dataset” (skill-focused).
Intermediate tutorials → “You will learn how to combine multiple datasets, resolve geospatial issues, and produce useful civic tech maps” (project-focused).
Beginners are relatively static — code snippets don’t change often.
Intermediates link to external repos, APIs, and evolving packages — they require ongoing maintenance (e.g., OSMnx or pysal updates).
Why This Matters for Your Checklists / Documentation
Beginner checklists should emphasize completion of small exercises (“Did the learner install pandas? Did they plot a histogram?”).
Intermediate checklists should emphasize workflow understanding and application (“Did the learner successfully join two datasets? Did they use CRS correctly? Did they map results with folium?”).
So, the intermediate ones need more conceptual checkpoints (grasp of why, not just how).
ExperimentsInHonesty commentedon Sep 2, 2025
Generic Markdown Template for Intermediate Tutorials
✅ Tutorial Creation Checklist
Template View
Intermediate Tutorial Checklist Template
Title & Introduction
Learning Objectives
Prerequisites
Setup Instructions
Core Concepts
Step-by-Step Walkthrough
Hands-On Exercise
Common Pitfalls & Troubleshooting
Discussion & Reflection
Next Steps / Advanced Topics
Glossary / Key Terms
References & Resources
Contributors
Template Copy
ExperimentsInHonesty commentedon Sep 9, 2025
@chinaexpert1 will create a checklist for assignees to self review code submissions
ExperimentsInHonesty commentedon Sep 9, 2025
@ExperimentsInHonesty will update intermediate checklist and templates with example checkbox from beginner
chinaexpert1 commentedon Sep 13, 2025
Instructions
chinaexpert1 commentedon Sep 20, 2025
I've been doing different templates for each tutorial series and putting them collapsed under
Details
in the Epic.