Lk learning goals

0_domain_study/README.md

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-# Domain Research

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.