The newly formed NextGen strives to attract and engage young enthusiasts on problems confronting modern data science. Our section of the journal serves as a platform to further that goal. Articles may describe novel methodologies, applied aspects, and may take the form of expository data analysis or writing, highlighting key facets of conducting and teaching statistics and data science. Case studies, data sets, historical perspectives, review articles, short videos, and statistical art projects are equally encouraged. Contributions from early-career statisticians, graduate/undergraduate students, K-12 kids and their teachers, and those new to data science are especially welcome. The honest value of our column is in creating a climate for young writing to flourish and thrive alongside those from seasoned practitioners, nurturing a spirit of hope and encouragement, and fostering the sense that no matter how deep you have ventured into data science, no matter how different your take on a problem is from established wisdom, if you have an intriguing tale to tell, you would be heard, and if you can write strong, you would be celebrated.

This section generally prioritizes work from students and early-career researchers (within six years of graduating with a terminal degree at the time of submission). Papers with senior co-authors (e.g., tenured professors) are welcome in our other sections.