This page (revision-20) was last changed on 08-Dec-2022 14:46 by Stefan

This page was created on 09-May-2022 10:38 by Stefan

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Version Date Modified Size Author Changes ... Change note
20 08-Dec-2022 14:46 3 KB Stefan to previous
19 17-Oct-2022 11:28 3 KB Stefan to previous | to last
18 17-Oct-2022 11:21 3 KB Stefan to previous | to last
17 22-Aug-2022 16:35 3 KB Sophia Botsch to previous | to last
16 22-Aug-2022 16:34 3 KB Sophia Botsch to previous | to last
15 22-Aug-2022 12:11 3 KB Jonas Umgelter to previous | to last
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12 22-Aug-2022 11:40 2 KB Jonas Umgelter to previous | to last
11 02-Aug-2022 14:52 2 KB Stefan to previous | to last
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9 27-Jun-2022 11:55 2 KB Jonas Umgelter to previous | to last
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6 14-Jun-2022 10:21 1 KB Stefan to previous | to last
5 13-Jun-2022 14:53 1 KB Jonas Umgelter to previous | to last
4 13-Jun-2022 14:46 1 KB Jonas Umgelter to previous | to last
3 12-May-2022 12:01 840 bytes Jurgen van den Hoogen to previous | to last
2 12-May-2022 12:00 1 KB Jurgen van den Hoogen to previous | to last
1 09-May-2022 10:38 108 bytes Stefan to last

Page References

Incoming links Outgoing links
Data Validation
Data Validation

Version management

Difference between version and

At line 3 changed one line
__Type of tool:__ e.g. web application or desktop application
__%%( color: #003399; font-size: 30px;)Data Validation:__
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__Required skills: __
Having high quality data has an enormous potential in improving the data flows within the processes of your company, and is vital in analyzing your data and interpret the results. Therefore, the data quality needs to be checked. For instance, the data may contain errors, missing values, wrongfully labeled data or low sampling rates from sensors. The data validation tool guides you through the important steps when assessing the quality of the data, and gives you tips and tricks how and what to adjust for improving the overall quality of your data. In addition, the Data Validation tool functions as a stepping stone towards analyzing your data using the Data Analytics tool.
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- Process/material knowledge: e.g. knowledge of material properties
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- Digitalization knowledge: e.g. no programming
__%%( color: #003399; font-size: 16px;)Type of tool:__ desktop application
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__Short description of the tool: __
__%%( color: #003399; font-size: 16px;)Short description of the tool: __ The Data Validation tool helps with general knowledge on validating the quality of your data. Each chapter gives tips and tricks for data quality.
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- Detailed description: Link to the guideline:
\\__%%( color: #003399; font-size: 16px;)Required skills: __
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__Use case/problem:__ Selecting material (recyclate) for specific
product requirements
- Process/material knowledge: e.g. knowledge of material properties
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__Description of the problem the tools solves:__ [[General] + [[Tool-specific]
- Digitalization knowledge: e.g. no programming
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__Disclaimer:__
\\__%%( 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)
At line 22 changed 2 lines
__How to use/download/access it:__ e.g. got the the gitup [[link], copy
the code into [[XY] and start using\\
__%%( color: #003399; font-size: 16px;)Disclaimer:__
(Disclaimer Text)
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__%%( color: #003399; font-size: 16px;)Screenshots:__
%%carousel
[Data Analytics/Di-Plast Data Validation Screenshot first page.JPG]
[Data Analytics/Di-Plast Data Validation Screenshot Introduction.JPG]
[Data Analytics/Di-Plast Data Validation Screenshot C1.JPG]
[Data Analytics/Di-Plast Data Validation Screenshot C2.JPG]
/%
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__Contact person of the tool: __
Stefan Bloemheuvel
\\__%%( color: #003399; font-size: 24px;)This tool supports you to:__
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__Related tools:__
- Analyse and Visualize your process data with data analytics -> [Data Analytics]
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- Get guidance to set up a working data infrastucture -> [Data Infrastructure Wiki]
\\__%%( color: #003399; font-size: 18x;)Example use case:__
\\
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- Find the right sensor to survey your process -> [Sensor Tool]
\\__%%( color: #003399; font-size: 24px;)Tool guideline and access: __
\\ - ⚠️ We recommend to open and save the user guideline before proceeding:
\\ - The tool can be accessed throughout the following link: [https://github.com/cslab-hub/data_validation]
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- Improve internal information and material flow -> [VSM]
\\__%%( 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).
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- Match material requirements with material properties -> [Matrix]
__Data Validation:__
Having high quality data has an enormous potential in improving the data flows within the processes of your company, and is vital in analyzing your data and interpret the results. Therefore, the data quality needs to be checked. For instance, the data may contain errors, missing values, wrongfully labeled data or low sampling rates from sensors. The data validation tool guides you through the important steps when assessing the quality of the data, and gives you tips and tricks how and what to adjust for improving the overall quality of your data. In addition, the Data Validation tool functions as a stepping stone towards analyzing your data using the Data Analytics tool.
The tool can be accessed throughout the following link: [https://share.streamlit.io/cslab-hub/data_validation/main/main.py]
__%%( 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]
\\
\\__%%( color: #003399; font-size: 16px;)After applying this tool:__
\\-->Analyse and Visualize your process data with data analytics -> [Data Analytics]
\\-->Get important insights in enhancing your data understanding
-> [Exploratory Pattern Analytics]
\\-->Match material requirements with material properties -> [Matrix]
This page (revision-20) was last changed on 08-Dec-2022 14:46 by StefanTop