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1 | | -# Learning Goals |
| 1 | +# 🎯 Learning Goals |
2 | 2 |
|
3 | | -## Collective |
| 3 | +This document outlines both our shared and individual learning |
| 4 | +objectives for this project. While each member may have different goals, |
| 5 | +aligning expectations early ensures stronger collaboration and more meaningful outcomes. |
4 | 6 |
|
5 | | -## Individual |
| 7 | +--- |
| 8 | + |
| 9 | +## 🤝 Collective Goals |
| 10 | + |
| 11 | +As a team, we aim to: |
| 12 | + |
| 13 | +- Strengthen our collaborative skills in a remote, cross-cultural setting. |
| 14 | +- Apply the data science lifecycle to a real-world problem |
| 15 | +from domain exploration to final communication. |
| 16 | +- Improve project planning and task ownership using |
| 17 | +GitHub tools (Issues, Projects, PRs). |
| 18 | +- Produce clean, reproducible, and well-documented code and analyses. |
| 19 | +- Deliver clear, impactful results through visualizations and |
| 20 | +stakeholder-oriented communication. |
| 21 | +- Build confidence in reviewing and giving feedback on technical work. |
| 22 | + |
| 23 | +--- |
| 24 | + |
| 25 | +## 👤 Individual Goals |
| 26 | + |
| 27 | +### **Fahed** |
| 28 | + |
| 29 | +- Improve GitHub fluency: project boards, CI/CD, and PR workflows. |
| 30 | +- Gain confidence in collaborative writing and documentation. |
| 31 | +- Practice clean Python code and code review habits. |
| 32 | +- Strengthen data visualization and storytelling skills. |
| 33 | +- Deepen understanding of exploratory data analysis (EDA) techniques. |
| 34 | +- Learn how to design and document a data pipeline collaboratively. |
| 35 | +- Gain confidence in managing raw datasets ethically and responsibly. |
| 36 | +- Improve research and domain framing skills. |
| 37 | +- Get familiar with reproducibility tools and Jupyter workflows. |
| 38 | +- Practice writing readable, reusable Python functions. |
| 39 | +- Focus on time management and structured task planning. |
| 40 | +- Strengthen understanding of data ethics and licensing. |
| 41 | +- Learn how to structure CI/CD for data science–oriented projects. |
| 42 | + |
| 43 | +--- |
| 44 | + |
| 45 | +### **Caesar** |
| 46 | + |
| 47 | +--- |
| 48 | + |
| 49 | +### **Maria** |
| 50 | + |
| 51 | +--- |
| 52 | + |
| 53 | +### **Mohammad** |
| 54 | + |
| 55 | +--- |
| 56 | + |
| 57 | +### **Terry** |
| 58 | + |
| 59 | +--- |
| 60 | + |
| 61 | +### **Tomas** |
| 62 | + |
| 63 | +--- |
| 64 | + |
| 65 | +> *"Learning is not attained by chance, it must be sought for with ardor."* |
| 66 | +> *— Abigail Adams (adapted)* |
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