In this course, students will gain a foundation in what is called “model thinking” (i.e., the art of exploring the statistical properties of a dataset so as to choose the most appropriate statistical model). The course will start with statistical first moments—means, medians, and distributions—so that the students all have a strong foundation in the basics. Then, it will gradually move up to statistical inference—which is about deriving modeled estimates and testing the soundness of one statistic versus another. In an effort to make the material more engaging and real-world applicable, the course will be taught, primarily, through R programming (a free, open-source programming language, most famously used for statistical analysis.

You'll walk away with

• The ability to understand datasets using statistical best practices.
• The ability to perform key statistical concepts like descriptive stats (means, medians, etc.), skew/kurtosis, correlation/covariance, regression, and categorical analysis.
• The ability to export statistical results to CSV or MS Excel files and write a statistical analysis.