retrospective

Author: MPKenley

0_domain_study/README.md

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-# Domain Research

collaboration/constraints

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+
+# Constraints
+
+This document outlines different constraints under which the project is
+implemented.These constraints can be external involuntary, internal
+involuntary and internal voluntary that impact
+planning, execution and collaboration of the project
+
+## External Constraints
+
+### ⏳ Deadlines
+
+• Each milestone should be done separately and tag after doing it.
+The deadline should be respected.Time waiting for reviewing can
+complicated the things.  
+• We are working in different time zones. That makes things difficult sometimes.
+
+### 🌐 The situation in some places
+
+• Some places don’t have stable internet connection all the time
+• The current political and social situation in the country of one of
+the members of group affects the planning and project execution sometimes.  
+
+### 📊 Data Access Constraints
+
+• Find relevant datasets on Haiti is challenging due to licensing or usage restrictions.
+
+## 🔐 Internal Involuntary Constraints
+
+Constraints that come from within the team but not intentionally.
+Often, related to availability and limited resources.
+
+### 🧠 Skill Gaps
+
+• In the group, we have different levels of experience in the field.
+But we miss a lot of knowledge to navigate the field easily.
+
+### 🚨 Unexpected Interruptions
+
+• Illness, emergencies, or personal events may impact participation.
+
+## 🧭 Internal Voluntary Constraints
+
+Team-agreed practices that support project quality, collaboration and learning.
+
+### 🙋 Task Preference
+
+• Members can express task preferences based on interest and learning goals.
+• Flexibility is encouraged to support cross-learning with peer help.
+
+### ✅ Review Checklist
+
+• All work must be reviewed before merging into the main branch.
+• A shared checklist ensures code quality and encourages helpful feedback.
+
+### 🔁 GitHub & Version Control Constraints
+
+• Code must be regularly committed to GitHub.
+• Use feature branches for separate tasks or features.
+• Pull requests (PRs) are mandatory for merges.
+• Branch names must be descriptive and relevant to the task/problem.

collaboration/learning_goals.md

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+
+# Learning Goals
+
+## Collective
+
+**Our shared goals for these exercises are**:
+
+Strengthen our collaboration and communication skills in a technical context by
+working through coding challenges together and exchanging ideas openly.
+
+Develop a consistent and professional workflow for writing, structuring, reviewing,
+ and improving code using shared standards and GitHub best practices.
+
+Write clear, readable, and well-documented solutions, including modular functions,
+meaningful commit messages, polished notebooks, and accessible READMEs.
+
+Support each other’s learning through constructive feedback, code reviews,
+ and active knowledge sharing—so everyone learns from everyone.
+
+Build confidence and creativity in how we approach problems as a team, experimenting
+with new techniques and tackling challenges with curiosity.
+
+Deepen our data-science foundations through weekly practice with EDA, visualization,
+ and fundamental statistics applied to real datasets.
+
+Create an inclusive, supportive learning environment where all voices are welcomed,
+questions are encouraged, and leadership is shared.
+
+Communicate insights clearly by translating technical results into simple explanations,
+ one-pagers, and concise presentations.
+
+Promote reproducible, future-friendly work through automation, versioning,
+structured pipelines, and documentation that helps future collaborators.
+
+## Individual Learning goal (Kervens)
+
+Build consistency and personal discipline by dedicating regular time to research,
+review, and independent practice.
+
+Apply personal agency tools to set clear intentions, monitor progress,
+and stay accountable throughout the project.
+
+Strengthen collaborative coding skills by mastering GitHub workflows
+(branching, pull requests, conflict resolution) and writing meaningful commit messages.
+
+Develop clear technical communication by maintaining well-commented scripts,
+structured notebooks, and visual explanations of decisions.
+
+Create reproducible workflows for data loading, cleaning, and modeling through
+modularized code and versioned notebooks.
+
+Apply data science to real-world questions—especially related to education, policy,
+or social outcomes, and document insights for both technical and non-technical audiences.