Welcome! This course brings together texts, visuals, and models for communicating data science, from research design to analysis-backed decisions.
“No one ever made a decision because of a number. They need a story.”
“The greatest value of a picture is when it forces us to notice what we never expected to see.”
“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”
The students I have in mind for this course are active learners. An active learner asks questions, considers alternatives, questions assumptions, and even questions the trustworthiness of the author or speaker. An active learner tries to generalize specific examples, and devise specific examples for generalities. An active learner doesn’t passively sponge up information — that doesn’t work! — but uses the readings and lecturer’s argument as a springboard for critical thought and deep understanding.
Along with commitments to active learning, be ever curious. Effective communication of data almost always require some form of transformation of those data into derived measures and estimates, and mapping those to visual variables. We handle transformation and mapping processes through computation. We will consider the how of transformation and mapping in the course, with the what, why, and for whom. Those involve critical thinking and logic: bring “them apples” to the course.
You are responsible for your learning, and your instructor — that’s me, Dr. Scott Spencer with Dr. Laura Scherling — will guide you on your journeys.
Best practices in conducting and communicating data analyses continuously flow, like water, carving new ideas along the banks of humanity. I test these ideas in personal projects and consulting for others, distill them, and bring them bespoke into our classroom.
If you see mistakes or want to suggest changes, please create an issue on the source repository.