Course Syllabus

As a bridge course, this course is distinct from the main course offerings of the School of Information – there are no syncronous discussion sections, and there are no graded assessments. Instead, this is a self-paced course of study designed to be completed in roughly a week before beginning a credit-bearing course.

The class works closely from R for Data Science to prepare students for the theoretical and applied work. We recommend the course for students preparing to take Statistics for Data Scientists (w203); but the course might also be a useful refresher for students who are taking Experiments and Causal Inference (w241) and Statistical Methods for Discrete Rseponse, Time Series, and Panel Data (w271).

In contrast to many methods of learning new languages, this course focuses on working with high-level parts of the language – namely plotting (from ggplot2) and data manipulation (from dplyr) before addressing lower-level parts of the language. Indeed, this course is at an even higher-level part of the langauge api than the R for Data Science textbook.

I (Alex) made this choice deliberately, based on a model of spoken language-learning: When learning a new language, we try to build concepts and vocabulary, even if this means that we conjugate verbs incorrectly or use inefficient methods of expressing ourselves. Often times, coding-language-learning tries to approach their language as though there is some axomatic truth from which the language is derived. These “truths” are enshrined in style guides, deployed code, and bravado; but they hide a more important truth – just start communicating and see what you can get done.

You can probably nagivate a busy market and appreciate the culture in a foreign land where you don’t speak the language. Similarily, you can probably write clumsy code that expresses your intent and learn from your data. Just start writing and learning; we’ll speak fluently soon enough.