Kedar Dabhadkar
Data scientist at Lam Research with >6 years of experience in statistical data analysis, engineering, and machine learning. Independently researches applications of LLMs and statistical modeling to science and engineering domains. Built Fast Dash, an open-source Python library that transforms Python functions into interactive web applications.
Sessions
TL;DR
Learn how to turn your Python functions into interactive web applications using open-source tools. By the end, each of us will have deployed a portfolio (or store) with multiple web applications and learned how to reproduce it easily later on.
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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 brought to light.
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 provide a walkthrough of the primary web application frameworks and showcase Fast Dash, an open-source Python library that we built to address specific prototyping needs.
This tutorial is designed for all data professionals who value the ability to quickly convert their scientific code into web applications. Participants will learn about the leading frameworks, their strengths and limitations, and a decision flowchart for picking the best one for a given task. We will go through some day-to-day applications and hands-on Python coding throughout the session. Whether you bring your use-cases and datasets, or pick from our suggestions, you'll have a reproducible portfolio (app store) of deployed web applications by the end!
Algal blooms threaten human health and aquatic ecosystems, making monitoring essential. While Chlorophyll a (Chl-a) effectively indicates algal presence, laboratory analysis is complex. This study utilizes satellite imagery as an alternative, addressing previous research limitations caused by scarce lab data. By combining the extensive Water Quality Portal dataset with Landsat satellite imagery, these models estimate Chl-a levels in New York's inland waters. Training with eight years of data demonstrated a strong correlation between satellite-derived and actual measurements (MAPE: 0.96%; RMSE: 3.2 μg/L), enabling improved spatial and temporal monitoring capabilities.