The article outlines eleven grand challenges in single-cell data science, highlighting the unique data science problems arising from the recent advancements in microfluidics and low sequencing costs. These challenges are categorized into transcriptomics, genomics, and phylogenomics, with each challenge addressing specific issues such as handling sparsity in single-cell RNA sequencing, defining flexible statistical frameworks for complex differential patterns, mapping single cells to reference atlases, and generalizing trajectory inference. The authors emphasize the need for scalable and statistically sound methods to handle the vast amounts of data generated, quantify measurement uncertainties, and integrate data from different types of measurements. They also discuss the importance of benchmarking and validating analysis tools to ensure the reliability and accuracy of single-cell data analysis. The challenges are designed to guide future research and development in single-cell data science, making it accessible to researchers from various fields.The article outlines eleven grand challenges in single-cell data science, highlighting the unique data science problems arising from the recent advancements in microfluidics and low sequencing costs. These challenges are categorized into transcriptomics, genomics, and phylogenomics, with each challenge addressing specific issues such as handling sparsity in single-cell RNA sequencing, defining flexible statistical frameworks for complex differential patterns, mapping single cells to reference atlases, and generalizing trajectory inference. The authors emphasize the need for scalable and statistically sound methods to handle the vast amounts of data generated, quantify measurement uncertainties, and integrate data from different types of measurements. They also discuss the importance of benchmarking and validating analysis tools to ensure the reliability and accuracy of single-cell data analysis. The challenges are designed to guide future research and development in single-cell data science, making it accessible to researchers from various fields.