How can we effectively and efficiently teach statistical thinking and computation to students with little to no background in either? How can we equip them with the skills and tools for reasoning with various types of data and leave them wanting to learn more?
This introductory data science course that is our (working) answer to these questions. The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on a consitent syntax (with tools from the
tidyverse), reproducibility (with R Markdown) and version control and collaboration (with git/GitHub). We help ease the learning curve by avoiding local installation and supplementing out-of-class learning with interactive tools (like
learnr tutorials). By the end of the semester teams of students work on fully reproducible data analysis projects on data they acquired, answering questions they care about.
This repository serves as a "data science course in a box" containing all materials required to teach (or learn from) the course described above.
xaringanslide decks, each to be covered roughly in a 75 minute class session
assignments: 6 homework assignments
labs: 10 guided hands on exercises for students requiring minimal introduction from the instructor
exams: 2 sample take-home exams and keys
project: Final project assignment
tutorials: Interactive learning exercises built with
website: This website includes links to all of the above and contains additional material for helping instructors set up their course.
Please feel free to submit an issue or a pull request for other resources to be listed here. See https://www.tidyverse.org/learn/ for other learning resources as well.
- Practical Data Science for Stats collection
- Curriculum Guidelines for Undergraduate Programs in Data Science
- ghclass (WIP)