0253 - Teaching Analytics Visually with Stefan Helfrich episode artwork

EPISODE · May 15, 2025 · 47 MIN

0253 - Teaching Analytics Visually with Stefan Helfrich

from Design Talk (dot IE)

Hosted by Christina Philips and Anabela Da Silva Filipe Soares.Welcome to today’s seminar by Stefan Helfrich.In this session Stefan talks about the education paths on offer for data analytics and the need for balance between learning concepts versus hands-on experiences with tools. Stefan makes the case for the value of visual workflow approaches for teaching and implementing analytics.How do we do that? KNIME implements a well-documented, comprehensive and capable software environment that enables users to design and operate data analytics workflows visually using the following objects:·       Nodes perform tasks on data. Nodes have inputs and outputs. Nodes have status/indicators. Nodes are natively implemented in Java. Python scripts may also be used as code nodes.·       Connectors link nodes. Connectors indicate data flows. Connectors send data from one node to another. Connectors have direction. Nodes plus connectors enable you to create workflows.·       Workflows are designed aggregates of nodes linked using connectors ·       Components/Metanodes encapsulate discrete sub-workflows. Component/metanodes can be used like nodes.·       A large library of pre-build nodes and metanodes are offered for common tasks like cleaning up data, visualization, plug into Tableau and PowerBI.·       Supports all types of data.·       Further reading, sources, mentions and acknowledgements.Stefan Helfrich -- https://www.linkedin.com/in/stefanhelfrich/  KNIME – https://www.knime.com/See the KNIME Educators Alliance and the Teaching Materials Repository.References:Berthold, M. R. (2019). What Does It Take to be a Successful Data Scientist? Harvard Data Science Review, 1(2)Further reading:For examples, additional teaching materials, sample curriculum, see “The Data Science Guide” – www.datascienceguide.orgUnless otherwise noted, the teaching materials (including workflow examples, code examples, and slides) are available under the Creative Commons Attribution 4.0 International license (CC BY 4.0).Music Title: Guitar HouseArtist: josh pan (2020)Source:  https://www.youtube.com/watch?v=JL-LId8ZWBMLicense: License CC BY 3.0Cover Art Title: We need You! Visual AnalyticsArtist: Nuno Machado and Allen HigginsSource: vignette_version.pptxLicense: CC BY-NC-SA 4.0Podcast LicenseDesign Talk (dot IE) CC BY-NC-SA 4.0 The license can be viewed at https://creativecommons.org/licenses/by-nc-sa/4.0By taking part you give permission for your voice to be recorded, for the recording to be edited, and for it to be posted and published as a podcast. Hosted on Acast. See acast.com/privacy for more information.

Hosted by Christina Philips and Anabela Da Silva Filipe Soares.Welcome to today’s seminar by Stefan Helfrich.In this session Stefan talks about the education paths on offer for data analytics and the need for balance between learning concepts versus hands-on experiences with tools. Stefan makes the case for the value of visual workflow approaches for teaching and implementing analytics.How do we do that? KNIME implements a well-documented, comprehensive and capable software environment that enables users to design and operate data analytics workflows visually using the following objects:·       Nodes perform tasks on data. Nodes have inputs and outputs. Nodes have status/indicators. Nodes are natively implemented in Java. Python scripts may also be used as code nodes.·       Connectors link nodes. Connectors indicate data flows. Connectors send data from one node to another. Connectors have direction. Nodes plus connectors enable you to create workflows.·       Workflows are designed aggregates of nodes linked using connectors ·       Components/Metanodes encapsulate discrete sub-workflows. Component/metanodes can be used like nodes.·       A large library of pre-build nodes and metanodes are offered for common tasks like cleaning up data, visualization, plug into Tableau and PowerBI.·       Supports all types of data.·       Further reading, sources, mentions and acknowledgements.Stefan Helfrich -- https://www.linkedin.com/in/stefanhelfrich/  KNIME – https://www.knime.com/See the KNIME Educators Alliance and the Teaching Materials Repository.References:Berthold, M. R. (2019). What Does It Take to be a Successful Data Scientist? Harvard Data Science Review, 1(2)Further reading:For examples, additional teaching materials, sample curriculum, see “The Data Science Guide” – www.datascienceguide.orgUnless otherwise noted, the teaching materials (including workflow examples, code examples, and slides) are available under the Creative Commons Attribution 4.0 International license (CC BY 4.0).Music Title: Guitar HouseArtist: josh pan (2020)Source:  https://www.youtube.com/watch?v=JL-LId8ZWBMLicense: License CC BY 3.0Cover Art Title: We need You! Visual AnalyticsArtist: Nuno Machado and Allen HigginsSource: vignette_version.pptxLicense: CC BY-NC-SA 4.0Podcast LicenseDesign Talk (dot IE) CC BY-NC-SA 4.0 The license can be viewed at https://creativecommons.org/licenses/by-nc-sa/4.0By taking part you give permission for your voice to be recorded, for the recording to be edited, and for it to be posted and published as a podcast. Hosted on Acast. See acast.com/privacy for more information.

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0253 - Teaching Analytics Visually with Stefan Helfrich

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Hosted by Christina Philips and Anabela Da Silva Filipe Soares.Welcome to today’s seminar by Stefan Helfrich.In this session Stefan talks about the education paths on offer for data analytics and the need for balance between learning concepts versus...

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