## Learning Outcomes

### Statistical and Computational Thinking

Students apply statistical knowledge and computational skills to formulate problems, plan data collection campaigns, or identify and gather relevant existing data then analyze the data to provide insights while using professional statistical analysis software packages.

### Algorithms and Software Foundations

Students employ algorithmic problem-solving skills to real-world problems. Students leverage existing packages and tools to solve their computational problems.

### Data Curation

Students prepare, stucture, and manage data from a variety of source and formats for use with a variety of statistical methods and models and should recognize how the quality of the data and the means of data collection may affect conclusions.

### Model Building and Assessment

Students employ informal modeling (such as data visualization, data aggregation, and summarization) and formal statistical and machine learning models while recognizing the strengths and weaknesses of proposed models.