07-08, 08:00–12:00 (US/Pacific), Ballroom C
Work not shown is work lost. Many excellent scientists and engineers are not always adept at showcasing their work. This results in many interesting scientific ideas that have never been seen in the light of day.
However, using today's tools, one no longer has to leave the Python ecosystem to create classy, complete prototypes using modern data visualization and web development tools. With over five years of experience building and presenting data solutions at huge science companies, we show it doesn't have to be challenging. We give a walkthrough of the primary web application frameworks and showcase Fast Dash, an open-source Python library we built to solve specific prototyping needs.
This tutorial is meant for all data professionals who find value in quickly turning their science code into web applications. Participants will learn about the leading frameworks, their strengths and limitations, and a framework for picking the best one for a given task. We will go through some day-to-day applications and hands-on tutorials in the final section.
Scientific analysis often remains hidden within code repositories, limiting its impact and accessibility. This tutorial bridges the gap between analysis and dissemination by demonstrating how to transform Python scientific code into engaging web applications without leaving the Python ecosystem. Drawing from five years of experience developing data solutions for major scientific organizations, we will showcase how modern tools make this process accessible to scientists and engineers regardless of web development background.
The tutorial begins with an exploration of why web applications are crucial for scientific communication, highlighting real-world examples where interactive visualization significantly enhanced understanding and engagement with complex data. We'll outline common barriers scientists face when trying to showcase their work and how web applications effectively overcome these challenges.
Next, we'll navigate the landscape of Python web application frameworks, comparing Dash, Streamlit, Gradio, Shiny, and Quarto. This section will provide a decision framework to help participants select the appropriate tool based on their specific needs, considering factors like required functionality, development time, and deployment context. Through guided examples, attendees will learn to identify which framework best serves different scientific scenarios.
The core of the tutorial focuses on Fast Dash, an open-source Python library we developed specifically for scientific prototyping needs. I'll explain how Fast Dash transforms Python functions into interactive web applications with minimal boilerplate code, emphasizing its advantages for geospatial visualization and scientific data presentation. Using a real-world case study of chlorophyll-a monitoring in New York's inland waters, we'll demonstrate how Fast Dash enabled the creation of an interactive dashboard that dramatically improved data accessibility compared to traditional static reporting methods.
The hands-on portion guides participants through building their own web applications. Starting with simple data visualization, we'll progress to interactive applications incorporating maps, charts, and user controls. Exercises will cover essential patterns like:
- Converting analytical functions to web interfaces
- Integrating multiple data sources
- Building effective interactive visualizations
- Handling user input and filtering
- Optimizing performance for larger datasets
The final section addresses deployment strategies and best practices for scientific web applications. Participants will learn about hosting options ranging from local development servers to cloud platforms, with specific attention to maintaining scientific integrity while enhancing accessibility.
Throughout the tutorial, we emphasize practical applications rather than theory. All examples come from real scientific workflows, demonstrating how interactive web applications can transform complex analyses into accessible tools. The methodology presented is applicable across disciplines, from environmental monitoring to genomics, machine learning, and beyond.
Participants will leave with a working knowledge of the Python web application ecosystem, hands-on experience with Fast Dash, and a framework for selecting and implementing the right tools for their scientific communication needs. Most importantly, they'll gain the confidence to showcase their scientific work effectively through interactive web applications, ensuring their valuable research reaches broader audiences and achieves greater impact.
At minimum, pip install fast-dash streamlit gradio
Prerequisites –Participants should have:
- Python 3.8+ installed
- Familiarity with basic Python programming
- A code editor or IDE of choice
- Git installed for accessing tutorial materials
Research Investigator and Data Scientist at Celanese Corporation (former DuPont)
PhD, Chemical Engineering, Graduate Minor in Statistical Machine Learning
I am a process engineer and data scientist with over six years of experience in statistical data analysis, engineering, and machine learning. I’m a Microsoft-certified data scientist and a Databricks-certified data engineer. I work in the Equipment Intelligence group at Lam Research, independently research LLM-based applications and machine learning for environmental monitoring.
I built and maintain Fast Dash, an open-source Python library that transforms Python functions into interactive web applications.