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  1. Semicolon: „How Data Science is driving digital transformation“ Semicolon: „How Data Science is driving digital transformation“ by Veit Schiele — published May 04, 2022 — last modified Nov 23, 2024 10:04 AM — filed under: Featured, Data analysis

    On 31 May, Veit Schiele will give a talk on „How Data Science Drives Digital Transformation“ in the Semicolon series at GFU.

    Located in About us / Meet us
  2. elena international: Web-based planning tool for microgrids elena international: Web-based planning tool for microgrids by Tim Weber — published Nov 06, 2019 — last modified Nov 23, 2024 10:05 AM — filed under: Energy, Research organisation, Data analysis

    For elena international we realise a web-based planning tool for decentralised groups of electricity sources, so called microgrids. Therefor wee use Jupyter ...

    Located in Blog
  3. Are Jupyter notebooks ready for production? Are Jupyter notebooks ready for production? by Veit Schiele — published Nov 05, 2019 — last modified Nov 23, 2024 10:40 AM — filed under: Featured, Agile Development, Data analysis, Platform Located in Blog
  4. Beuth University: Prototype for a medication app Beuth University: Prototype for a medication app by Tim Weber — published Nov 14, 2019 — last modified Nov 23, 2024 10:05 AM — filed under: Research organisation, Healthcare, Data protection and data safety, Data analysis Located in Blog
  5. Choosing the right NoSQL database Choosing the right NoSQL database by Veit Schiele — published Aug 28, 2020 — last modified Nov 23, 2024 10:06 AM — filed under: Software architecture, Featured, Open Source, Data analysis

    Relational databases dominated the software industry for a long time and are very mature with mechanisms such as redundancy, transaction control and standard ...

    Located in Blog
  6. Find data and its origin with DataHub Find data and its origin with DataHub by Veit Schiele — published Aug 17, 2021 — last modified Nov 23, 2024 10:07 AM — filed under: Featured, Data analysis

    One of the big difficulties for data scientists is finding the data they need, understanding it and assessing its trustworthiness. Without the necessary ...

    Located in Blog
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