Treffer: Hamed-Ahmadinia/data-analytics-python-course: Update – Week 8 Materials & Final Presentation

Title:
Hamed-Ahmadinia/data-analytics-python-course: Update – Week 8 Materials & Final Presentation
Authors:
Publisher Information:
Zenodo
Publication Year:
2024
Collection:
Zenodo
Document Type:
E-Ressource software
Language:
English
DOI:
10.5281/zenodo.15270509
Rights:
GNU General Public License v3.0 or later ; gpl-3.0-or-later ; https://www.gnu.org/licenses/gpl-3.0-standalone.html
Accession Number:
edsbas.871BA7A7
Database:
BASE

Weitere Informationen

📦 Release Notes – Version 2.0.0 Official Full Course Release – Week 8 Materials Finalized 📅 Released by Hamed-Ahmadinia 🔖 Tag: v2.0.0 🔁 Changes since: v1.0.0 ✅ What's New in This Release This milestone release marks the completion and full packaging of the course, including Week 8 content and final presentations. 🆕 Added 🗂 Week 8 Folder now includes: Final_Mini_Project_Presentation___Full_Announcement_with_Schedule.pdf – a full guide for the mini-project showcase and student announcement. session_timer.html – a clean HTML countdown timer for session management and presentations. 📚 Course Structure Finalized All weekly folders (Week 1 to Week 8) now complete with materials. Final polish applied to: MiniProjectInstruction/ directory Core materials for group work and assessment 📥 Assets Included Sample datasets for practical data analysis Python notebooks and resources used during sessions Instructor and institution information for context 🛠 Tools & Guidance Installation instructions updated Course delivery tools such as the countdown timer and presentation templates added 🎯 Ideal For: Students following along the full 8-week course Instructors wishing to replicate the course in other institutions Reviewers assessing project-based learning using real datasets in Python 📘 Documentation Refer to the updated README for: Full session-by-session breakdown Syllabus and learning objectives Course philosophy and pedagogical approach 🧠 Next Steps: Future versions may include: Feedback summary and student reflections Improvements based on course evaluations Additional modules (e.g., intermediate Python or visualization extensions)