Codeless Data Science with KNIME: Data Analysis and Optimization Without Coding

Abstract

In the era of digital transformation, data analysis skills have become
essential competencies for students and the general public. However,
data science learning is often perceived as complex due to its reliance
on programming skills. To address this issue, a community
engagement program in the form of a webinar and workshop entitled
“Codeless Data Science Using KNIME: Data Analysis and
Optimization without Coding” was conducted. This program aimed
to enhance participants’ literacy and understanding of fundamental
data science concepts through a codeless approach using the KNIME
Analytics Platform. The activity was implemented in two main stages:
a webinar session focusing on conceptual explanations and the data
science workflow, followed by a hands-on workshop session involving
practical data analysis and predictive modeling through visual
workflows without coding. Program evaluation was conducted using
a post-activity feedback questionnaire. The results indicated that
69.4% of participants were very satisfied, 22.2% were satisfied, 5.6%
felt neutral, and 2.8% were dissatisfied with the overall
implementation. These findings demonstrate that the majority of
participants responded positively to the materials, delivery methods,
and overall organization of the activity. Therefore, this community
engagement program can be considered effective in promoting
inclusive and accessible data science learning through a codeless
approach.