Academic Breakthroughs with Low Code
Our fortnightly selection of must-reads from the community, for the community
Low-code platforms are transforming academia by enabling researchers with limited programming skills to develop applications more easily. These tools simplify tasks like data analysis, visualization, and modeling, making complex methods more accessible. This shift enables researchers to concentrate on their work rather than coding details, speeding up discovery and cross-discipline collaboration. In education, low-code platforms help students create interactive projects and prototypes, enhancing their practical skills and engagement with technology.
The articles that we selected for this edition of the Workflow focus on applications of KNIME in academia, either for research or teaching. From a fascinating research around geospatial analytics to explain the decline in the number of pharmacies in Germany, to a collection of experiences using KNIME for teaching business analytics at the University of New Brunswick, you’ll certainly learn one thing or two. The last story collects valuable data science tips and tricks shared by the KNinjas of the past JustKNIMEIt edition. Happy reading!
Who closed up shop? A geospatial analysis of pharmacy decline in Baden-Württemberg with KNIME
The number of pharmacies in Germany has been declining for years. It is often claimed that it is mainly pharmacies in rural areas that are closing. Using the example of pharmacies in Baden-Württemberg, in this article Dr. Christian Knobloch shows how KNIME and the Geospatial Analytics extension for KNIME can be used to analyze whether there are structural differences between the remaining pharmacies and those that have closed. The author reads in scraped Google business profiles of pharmacies and information about municipal boundaries and spatial organization from state and federal institutions, processes it, performs spatial joins, calculates distances between all pharmacies and displays insights on a multi-layer geo-visualization with the Kepler.gl framework. A great data story that you should not miss!
Teaching Business Analytics with KNIME at a Business School
By Dongmin Kim and Jong-Kyou Kim
Teaching business analytics is increasingly prominent in university curricula due to its critical role in modern decision-making processes. As businesses generate vast amounts of data, the ability to analyze and derive actionable insights becomes essential for competitive advantage. This has led to a surge in business analytics programs, equipping students with skills in data analysis, statistical methods, and predictive modeling. In this article, Dongmin Kim and Jong-Kyou Kim from the University of New Brunswick share their experiences using a low-code tool like KNIME to teach business analytics in Management Information Systems courses. They introduce KNIME and a few key features, discuss student feedback, and conclude with their future plans to advance the teaching of business analytics in academic settings.
9 data science superstars share tips & tricks
By Aline Bessa
The data science superstars from the KNIME community are a special kind of superstar. They share their work, experience, and knowledge to support others. The nine winners of the past edition of JustKNIMEIt posted solutions to all 30 challenges and contributed hugely to the community by sharing efficient, well-documented, and creative workflows on the KNIME Forum. In this article, Aline Bessa collects 9 data science tips and tricks shared by the nine winners: from how to manage projects, to favorite data science techniques, becoming part of a community, go-tos for visualization hacks, web scraping and more. Don’t miss out the tips and join the current season of JustKNIMEIt — you could be the next KNinja! 🙂
We love learning new creative solutions using KNIME from the articles that we publish, and we love to share them with you. We are proud of building together a thriving community that supports each other, shares experiences, and shapes the future of low code data science.
See you in the next Workflow,
The Editors of Low Code for Data Science
