This page (revision-56) was last changed on 19-Sep-2022 15:18 by Stefan

This page was created on 12-May-2022 12:00 by Jurgen van den Hoogen

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Page References

Incoming links Outgoing links
Data Analytics
Data Analytics

Version management

Difference between version and

At line 1 changed one line
__Process Data Analytics:__
%%viewer [https://player.vimeo.com/video/670141220?h=3e15012ff4] %%
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__%%( color: #003399; font-size: 30px;)Data Analytics:__
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The files for the tool can be downloaded at:  [https://github.com/cslab-hub/Data_Analytics_DIPLAST|https://github.com/cslab-hub/Data_Analytics_DIPLAST]
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\\__%%( color: #003399; font-size: 16px;)Type of tool:__ desktop application to install \\
\\__%%( color: #003399; font-size: 16px;)Short description of the tool: __
The tool is meant to guide users into starting with using Data Analytics at their organization.\\
\\__%%( color: #003399; font-size: 16px;)Required skills: __
- Process/material: e.g. knowledge of material properties\\
- Digitalization: Installation of programming tools\\
\\__%%( color: #003399; font-size: 16px;)Required programs %%( color: #003000; font-size: 14px;)(step-by-step guide and links provided in user guideline blow): __
\\- Python
\\- Java
\\- Anaconda
\\- Tool files from the GitHub (link below)
\\__%%( color: #003399; font-size: 24px;)This tool supports you to:__
- Visualize process data
- Compare two production runs (one with virgin material and one with recyclate)
- Explore patterns inside a dataset
\\__%%( color: #003399; font-size: 18x;)Example use case:__
After validating your data with help of e.g., the data validation tool, we can begin analyzing to get further insights.
The Data Analytics tool is an interactive dashboard that helps with interpreting the variables in your dataset with help of visuals.
By providing your own data in the 'data' folder, several analytical options are at your disposal.
For example, a correlation plot that gives insights into which variables are behaving similarly, or a PCA analysis that will help determining the most influential variables in your dataset.
\\__%%( color: #003399; font-size: 24px;)Tool guideline and access: __
\\ - ⚠️ We recommend to open and save the user guideline before proceeding: [Data Analytics/Di-Plast_Data Analytics_Installation Guideline.docx]
\\- Get the code/installation files from [https://github.com/cslab-hub/DataAnalytics_Diplast]
\\__%%( color: #003399; font-size: 16px;)Contact person of the tool: __
Jurgen van den Hoogen [mailto:j.o.d.hoogen@jads.nl] & Stefan Bloemheuvel [mailto:s.d.bloemheuvel@jads.nl] form the Jheronimus Academy of Data Science (JADS).
__%%( color: #003399; font-size: 16px;)Screenshots:__
%%carousel
[Data Analytics/data_analytics_home.png]
[Data Analytics/data_analytics_feature_selection.png]
[Data Analytics/data_analytics_comparison.png]
[Data Analytics/data_analytics_final_data_report.png]
/%
\\
\\
__%%( color: #003399; font-size: 24px;)Related tools:__
__%%( color: #003399; font-size: 16px;) Before applying this tool:__
\\We recommend also taking a look at the following Di-Plast tools below. They can help you to gather necessary information and data, help to better prepare your data and continue working with it afterwards:
\\--> Improve internal information and material flow -> [VSM]
\\--> Get guidance to set up a working data infrastructure -> [Data Infrastructure Wiki]
\\--> Find the right sensor to survey your process -> [Sensor Tool]
\\--> Validate your process data -> [Data Validation]
\\
\\__%%( color: #003399; font-size: 16px;)After applying this tool:__
\\-->Get important insights in enhancing your data understanding
-> [Exploratory Pattern Analytics]
\\-->Match material requirements with material properties -> [Matrix]
This page (revision-56) was last changed on 19-Sep-2022 15:18 by StefanTop