Module 3: Enterprise Investment Dashboards with Dash
Part of Specialization: Data Visualization Using Python
Self-paced - Mentor-led - 7 weeks - 3 Modules
Module 3: Enterprise Investment Dashboards with Dash
🕑 4 Lessons · Deliverable: Production-grade Dash investment application
Streamlit gets your dashboard in front of stakeholders fast. But some projects demand more: precise layout control, complex interactivity, and a codebase that a team can maintain at scale. That is where Dash earns its place.
Module 3 is built for analysts who want to move beyond prototyping into production. You already have a working Streamlit investment dashboard and, from Module 1, a network analysis of financial news sentiment. Now you rebuild the dashboard in Dash with precise callback control — then close the loop by merging in the NLP sentiment signal you built in Module 1, so the finished application reflects the full arc of the course: news to network to number to decision.
From Streamlit to Dash — Why the Upgrade Matters
A direct architectural comparison. When Streamlit’s script model hits its limits. The callback model explained without jargon. Scoping a Dash project from a Streamlit baseline.
Dash Layout, Callbacks, and Interactivity
Building a multi-component Dash investment application. app.layout, @app.callback, Input/Output chaining. The precise, responsive interactivity that enterprise stakeholders expect.
Closing the Loop — Integrating NLP Sentiment Signals into Your Dashboard
Merging Module 1’s entity and sentiment output into your Dash fundamentals table. A new callback surfaces the sentiment layer alongside price and fundamentals — the moment the three-module arc comes together in one application.
Deploying, Presenting, and Publishing Your Portfolio Piece
Deploying the completed application to Render. Structuring the finished project for GitHub and your portfolio site. Presenting the investment application live — to a non-technical audience, under pressure.
📄 Module 3 Deliverable: A production-grade Dash investment application combining your Module 2 fundamentals dashboard with the NLP sentiment signal from Module 1. Deployed and publicly accessible. Annotated code structured for team readability.
📚 Note: Module 3 builds directly on the network sentiment output from Module 1 and the Streamlit dashboard from Module 2.
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