The recent surge in microfluidics and combinatorial indexing strategies, along with low sequencing costs, has enabled single-cell sequencing technology. This has led to a data revolution in single-cell biology, posing unique data science challenges. The authors outline eleven key challenges for advancing single-cell data science (SCDS). These challenges span transcriptomics, genomics, and phylogenomics, and include issues such as handling sparsity in scRNA-seq, quantifying measurement uncertainty, integrating data across samples and experiments, and scaling to higher dimensionalities. The paper emphasizes the need for computationally efficient and statistically sound methods to manage the vast amounts of data generated by single-cell sequencing. It also highlights the importance of benchmarking and validating analysis tools. The challenges are categorized into recurring themes and specific challenges, with a focus on the need for flexible statistical frameworks, reliable reference systems, and generalizing trajectory inference. The paper also discusses the importance of integrating multiple types of data and the need for methods that can handle the unique challenges of single-cell data, such as high sparsity and technical noise. The authors conclude that SCDS is entering a new era, requiring innovative solutions to address the complex data science problems arising from single-cell sequencing.The recent surge in microfluidics and combinatorial indexing strategies, along with low sequencing costs, has enabled single-cell sequencing technology. This has led to a data revolution in single-cell biology, posing unique data science challenges. The authors outline eleven key challenges for advancing single-cell data science (SCDS). These challenges span transcriptomics, genomics, and phylogenomics, and include issues such as handling sparsity in scRNA-seq, quantifying measurement uncertainty, integrating data across samples and experiments, and scaling to higher dimensionalities. The paper emphasizes the need for computationally efficient and statistically sound methods to manage the vast amounts of data generated by single-cell sequencing. It also highlights the importance of benchmarking and validating analysis tools. The challenges are categorized into recurring themes and specific challenges, with a focus on the need for flexible statistical frameworks, reliable reference systems, and generalizing trajectory inference. The paper also discusses the importance of integrating multiple types of data and the need for methods that can handle the unique challenges of single-cell data, such as high sparsity and technical noise. The authors conclude that SCDS is entering a new era, requiring innovative solutions to address the complex data science problems arising from single-cell sequencing.