Python Jupyter Notebooks and Smart Factory Datasets for Interactive Exercises in Course Fundamentals of Computer Science
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2024
|
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901826920513536 |
|---|---|
| author | Seiger, Ronny |
| author_facet | Seiger, Ronny |
| contents | <p>This is the official material to accompany the paper "Teaching Computer Science Fundamentals to Business Students: A SoTL Experience Report on How to Increase Student Engagement via Practical Programming Tasks and AI" published in the proceedings of GeNeMe 2024 (DOI: <a href="https://doi.org/10.25368/2025.121" target="_blank" rel="noopener">10.25368/2025.121</a>).</p> <p>This repository contains Jupyter (Python) notebooks with the newly introduced in-class interactive programming exercises as presented in the paper. For each lecture part, you can find a notebook that just contains the tasks and code skeletons, and a notebook with the corresponding exemplary solutions. </p> <p>The tasks are based on data and use cases involving a smart factory. The specific datasets used in the individual programming exercises are also included in this repository.</p> <p> </p> <p>How to use:</p> <p>We recommend downloading all the files into a new folder and opening the folder with Visual Studio Code. Alternatively the files can be uploaded to a Jupyter environment, e.g., Google Colab or used locally in Anaconda.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_12177764 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Python Jupyter Notebooks and Smart Factory Datasets for Interactive Exercises in Course Fundamentals of Computer Science Seiger, Ronny <p>This is the official material to accompany the paper "Teaching Computer Science Fundamentals to Business Students: A SoTL Experience Report on How to Increase Student Engagement via Practical Programming Tasks and AI" published in the proceedings of GeNeMe 2024 (DOI: <a href="https://doi.org/10.25368/2025.121" target="_blank" rel="noopener">10.25368/2025.121</a>).</p> <p>This repository contains Jupyter (Python) notebooks with the newly introduced in-class interactive programming exercises as presented in the paper. For each lecture part, you can find a notebook that just contains the tasks and code skeletons, and a notebook with the corresponding exemplary solutions. </p> <p>The tasks are based on data and use cases involving a smart factory. The specific datasets used in the individual programming exercises are also included in this repository.</p> <p> </p> <p>How to use:</p> <p>We recommend downloading all the files into a new folder and opening the folder with Visual Studio Code. Alternatively the files can be uploaded to a Jupyter environment, e.g., Google Colab or used locally in Anaconda.</p> |
| title | Python Jupyter Notebooks and Smart Factory Datasets for Interactive Exercises in Course Fundamentals of Computer Science |
| url | https://doi.org/10.5281/zenodo.12177764 |