Online Graduate Certificate in Statistics for Data Science

An introduction to the principles and practices of effective data visualization as an essential skill for learning from data and for communicating with others, both within and outside of an organization. Students will combine principles from statistics, psychology, and computer science to design and create visualizations and use them to communicate with and about data to stakeholders and others. Prerequisites: Intro Programming.

This course takes a practical approach to the fitting, assessment, interpretation, and presentation of statistical models, introducing students to a broad range of applied regression methods via case studies with real datasets. Topics include data preparation and visualization, the generalized linear model framework, and specific examples of frequently used models (such as multiple regression, regression for count and binary data, generalized additive models, and models to account for hierarchical structure or dependence).
Prerequisites: Intro Statistics.

An introduction to matrix algebra methods useful in data data science and their implementation in software. Topics include representing data using matrices and higher dimensional arrays, transforming data in these formats, matrix decompositions, and applications of linear algebra in data science.

An introduction to the principles of causal inference and statistical design. Students will learn principles that can guide the collection of data and the creation and interpretation of models. Prerequisites: DATA 545.

An introduction to optimization from a statistical perspective, with emphasis on computation and problem-solving. Computer software will be used to explore practical examples. Prerequisites: DATA 555.

An introduction to simulation for statistics and data science, with emphasis on computation and practical examples. Computer software will be used to explore practical applications. Prerequisites: Intro Programming, DATA 545 or 502.