Adam Kulidjian - Crafting Impactful Dashboards for Your Clients

Python

Crafting impactful dashboards requires understanding client needs and requirements. Learn best practices for data visualization with Python and its libraries.

Key takeaways
  • Crafting impactful dashboards requires understanding the client’s needs and clarifying the requirements.
  • Use diagramming to explore relationships between variables and identify patterns.
  • Consider caching data to improve performance and reduce computational overhead.
  • Use plotly for data visualization and filtering.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line values.
  • Use visual variables such as color, shape, and size to convey information.
  • Consider using Figma for design and prototyping.
  • Use Python and its libraries for data analysis and visualization.
  • Clarify the requirements and iterate on the design to ensure it meets the client’s needs.
  • Use diagramming to identify relationships between variables and create a visual correspondence.
  • Consider using Gantt charts and Sploms for data visualization.
  • Use caching tools to improve performance and reduce computational overhead.
  • Prioritize user experience and make it easy to compare regression line

More talks

Develop AI agents with Semantic Kernel - Jakob Ehn - NDC Oslo 2024

Automate Your Kitchen with Python & Applied AI — Sena Sahin

How to fall in love with TDD - Gui Ferreira - NDC Oslo 2024

Avrahami - The Internet's Best Experiment Yet | PyData Global 2023

Philipp Rudiger Rapidly prototyping and deploying powerful data applications in Jupyter using Pane

RustcContributor::explore @compiler-errors session: method selection

Connor Stone - AstroPhot: Fitting Everything Everywhere all at Once in Astronomical Images

Juan Nunez-Iglesias - View, annotate, and analyze multi-dimensional images in Python with napari