<![CDATA[Purpose: This study aims to evaluate the effectiveness of an online Associate Data Scientist training program that utilizes RapidMiner as the primary platform for teaching data science and machine learning. The goal is to assess participants' improvements in data preprocessing, algorithm application, and model evaluation skills. Methodology/approach: The training program was conducted via Zoom and included interactive lectures, live demonstrations, hands-on exercises, and individual assignments. RapidMiner was used as the main tool throughout the sessions. Participants were evaluated through tasks assigned in each session and a final project that required them to analyze a dataset, apply relevant algorithms, and assess model performance. Results/findings: The results showed significant improvement in participants’ technical understanding and application skills. The average final project score was 87.0, indicating strong competence in data handling, algorithm selection, and model evaluation. Most participants completed the project successfully, demonstrating their readiness to apply data science concepts in real-world scenarios. Conclusions: The online training effectively bridged the gap between theory and practice, proving that remote learning can deliver quality outcomes in technical education. The combination of RapidMiner and a structured training format enabled participants to gain applicable skills in data science. However, improvements in instructional delivery and interaction are still needed to optimize learning experiences. Limitations: Challenges included internet connectivity issues and limited real-time interaction, which sometimes hindered learning flow and instructor support. Contribution: This study provides valuable insights into data science education, proving that online programs with practical tools like RapidMiner can successfully build core competencies in aspiring data professionals.]]>
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