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mech-m-dual-1-dbm

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for data science. All the topics are presented with the Python implementation included. We cover matrix decompositions, regression, signal processing, sparsity and compressed sensing and a bit of statistics.

https://github.com/kandolfp/mech-m-dual-1-dbm

Science Score: 54.0%

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    Low similarity (11.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for data science. All the topics are presented with the Python implementation included. We cover matrix decompositions, regression, signal processing, sparsity and compressed sensing and a bit of statistics.

Basic Info
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 1
  • Open Issues: 4
  • Releases: 2
Created over 1 year ago · Last pushed 8 months ago
Metadata Files
Readme License Citation Zenodo

README.md

MECH-M-DUAL-1-DBM - Grundlagen datenbasierter Methoden

Course material for a ~3 * 15 hours (5 ECTS) course on basic concepts for data science. All the topics are presented with the Python implementation included. We cover matrix decompositions, regression, signal processing, sparsity and compressed sensing and a bit of statistics.

Citing this project

Citation information

DOI

Development

We use Quarto to generate the lecture material. Where we are creating a book, see docs for the structure. In short, each part has its own folder where you find the qmd files and everything is managed via _quarto.yml. In order to make the use easy the entire project is managed with pdm so to start the preview run

bash pdm sync pdm quarto preview

The project is also compatible with the VSCode extension of Quarto, just make sure the the Python environment in ./.venv is used.

[!IMPORTANT] In one example locale.setlocale(locale.LC_ALL, 'de_AT.utf8') is used so make sure the language is installed on your system to make this example run.

Publishing

After pushing the published website will automatically be built and deployed at kandolfp.github.io/MECH-M-DUAL-1-DBM/. Due to the dynamic nature of the material this might take a couple of minutes.

You can also create a pdf by calling pdm run quarto render --to pdf

or the html version pdm run quarto render --to html

You can also find a pdf in the releases

Owner

  • Login: kandolfp
  • Kind: user

Citation (CITATION.cff) 

title: MECH-M-DUAL-1-DBM - Grundlagen datenbasierter Methoden
abstract: >-
  Course material for a ~3 * 15 hours (5 ECTS) course on
  basic concepts for data science. All the topics are
  presented with the Python implementation included. We
  cover matrix decompositions, regression, signal
  processing, sparsity and compressed sensing and a bit of
  statistics.
authors:
- family-names: Kandolf
  given-names: Peter
  orcid: 'https://orcid.org/0000-0003-3601-0852'
cff-version: 1.2.0
identifiers:
  - type: doi
    value: 10.5281/zenodo.14671708
keywords:
- Python
- matrix computations
- matrix decompositions
- regression
- signal processing
license: CC-BY-NC-SA-4.0
version: v1.0.2
date-released: '2025-06-27'

GitHub Events

Total
  • Create event: 7
  • Release event: 3
  • Issues event: 20
  • Delete event: 4
  • Issue comment event: 5
  • Push event: 113
Last Year
  • Create event: 7
  • Release event: 3
  • Issues event: 20
  • Delete event: 4
  • Issue comment event: 5
  • Push event: 113

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 4
  • Total pull requests: 0
  • Average time to close issues: about 2 hours
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 0
  • Average time to close issues: about 2 hours
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ziselsberger (5)
  • io-peter (3)
  • kandolfp (3)
  • MrP123 (2)
  • lennypoell (2)
  • lukaswerlberger (1)
Pull Request Authors
  • MrP123 (1)
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Dependencies

.github/workflows/publish.yml actions
  • JamesIves/github-pages-deploy-action releases/v3 composite
  • actions/checkout v4 composite
  • pdm-project/setup-pdm v4 composite
pyproject.toml pypi
  • imageio >=2.35.1
  • jupyter >=1.1.1
  • matplotlib >=3.9.2
  • pandas >=2.2.3
  • plotly >=5.24.1
  • pyqt5 >=5.15.11
  • quarto-cli >=1.5.57
  • scipy >=1.14.1