In the age of the data revolution, the civil engineer can enhance the management of infrastructure systems using new techniques focused on data. This dissertation present three studies in which data science approaches are used to enhance management of water and sanitation systems in both the built and natural environments. Chapters 1 and 2 focus on improving methods for data collection relating to water quality monitoring. In Chapter 1, the efficacy of different water quality sampling program designs is evaluated as the programs relate to meeting monitoring goals. Considerations include how timing, location, and distribution system operations can affect monitoring program outcomes. In Chapter 2, a framework for water quality monitoring program development based on a systematic understanding of potential hazards (The Hazard Based Water Quality Monitoring Planning Framework) is developed and tested for a large and important watershed in Massachusetts. A method for leveraging long-term datasets to evaluate sampling frequencies is also presented. Chapter 3 focuses on geospatial data processing and machine learning techniques that can be used to predict locations of buried sanitation infrastructure. A path forward for scaling up that work to the national level in the United States is presented. These studies provide strong examples of how the future of the field of civil engineering can be improved using data science.