The goal of Journal of DataLink (JDL) is to advance the field of data science education through a platform that provides high-quality research, innovative methodologies, and practical insights. Our aim is to bridge the gap between theoretical research and practical application, focusing on the educational value of data in real-world scenarios.

We welcome submissions on, but not limited to, the following topics:

• Real Data Analysis:
  • Research utilizing real data from various fields such as healthcare, finance, social sciences, engineering, environmental sciences, etc.
  • Case studies applying data science techniques to solve practical problems.
• Synthetic Data Generation:
  • Studies on the creation and validation of synthetic datasets that accurately reflect the characteristics of real data.
  • Comparative analysis of synthetic and real data in educational settings.
• Data Processing and Curation for Educational Purposes:
  • Techniques for preprocessing and curating data for educational purposes.
  • Datasets specifically designed for data science education.
• Innovative Teaching Methodologies:
  • Development and assessment of teaching methodologies in data science education.
  • Design and implementation of data science curricula across various educational levels.
• Modeling and Analytical Techniques:
  • New modeling techniques and their applications in an educational context.
  • Practical examples of data analysis using the latest tools and technologies.
• Educational Tools and Resources:
  • Development of software, tools, and resources that enhance data science education.
  • Reviews of existing educational tools and their impact on learning outcomes.
• Multimedia and Interactive Learning:
  • Integration of video, interactive tutorials, and practical exercises in data science education.
  • Studies on the effect of multimedia resources on learning engagement and performance.