Upon satisfactory completion of this course students will be able to:

•  demonstrate appreciation of, and fluency with, central topics and tools of  statistical learning;
• apply a variety of statistical models to data, utilizing the R programming language or Python, and assess those models for flexibility, predictive accuracy and explanative power;
• use computational methods such as bootstrapping to make estimates of model parameters;
• distinguish most suitable modeling methods for particular data sets;
• exhibit technical & intuitive understanding of various data models and their assumptions and trade-offs;
• exhibit competence in applying a variety of statistical models to a variety of real-world data