Dash
Dash is designed quite well for its purpose, so there is not really much room for large improvements, however there are some ideas that come to mind in terms of potential improvements that could be added.
Firstly, in order to implement Dash, an organization or user is required to code the entire backend of the dashboard in Python. This raises the level of difficulty in terms of implementation for professional organizations, who want to implement a dashboard but do not necessarily have the coding expertise on their current team. A potential solution to this would be creating a simple graphical user interface for Dash, which would make it easier to build Apps with it, like how Tableau works. However this would require significant time investment to bring to fruition. Another potential weakness is that Dash make not protect the underlying data of the dashboard as well as a more commercial solution like Tableau.
Further, there is a lot of room for improvement in terms of the features that could be implemented into dash. This site contains an interesting comparison between the features offered by Tableau compared to Dash. There are several things that could be added to Dash such as better data management, real time analytics, or third party integrations.
You can find several examples of Dash apps here.
As Dash is very useful for AI and data science related apps, it appears that there are equal use cases in industry and academia. However it’s easier to find app examples that appear to be created for academic purposes. This may be because Dash is harder to use right out of the box relative to Tableau or other BI tools, which would make it harder for professional organizations to implement with ease.
Dash is designed quite well for its purpose, so there is not really much room for large improvements, however there are some ideas that come to mind in terms of potential improvements that could be added.
Firstly, in order to implement Dash, an organization or user is required to code the entire backend of the dashboard in Python. This raises the level of difficulty in terms of implementation for professional organizations, who want to implement a dashboard but do not necessarily have the coding expertise on their current team. A potential solution to this would be creating a simple graphical user interface for Dash, which would make it easier to build Apps with it, like how Tableau works. However this would require significant time investment to bring to fruition.
Another potential weakness is that Dash make not protect the underlying data of the dashboard as well as a more commercial solution like Tableau.
Further, there is a lot of room for improvement in terms of the features that could be implemented into dash. This site contains an interesting comparison between the features offered by Tableau compared to Dash. There are several things that could be added to Dash such as better data management, real time analytics, or third party integrations.
You can find several examples of Dash apps here. A few that are very interesting and relevant include:
These three key examples are all relevant as they are all related to data science or machine learning in some way. While the first is an example of classical machine learning, the former two are examples of deep learning. Further, these apps can help a user understand machine learning or deep learning from a visual aspect which can be helpful for learning purposes.
Here are some additional examples that are not related to data science, but help show what Dash is also capable of:
As Dash is very useful for AI and data science related apps, it appears that there are equal use cases in industry and academia. However it’s easier to find app examples that appear to be created for academic purposes. This may be because Dash is harder to use right out of the box relative to Tableau or other BI tools, which would make it harder for professional organizations to implement with ease.
Academic use of Dash is also attractive since it can be used to visualize concepts, making them easier to teach. This can be seen in the previous example dashboards linked above.
Some useful links are included below if you wish to learn more.