Hello and welcome to DCS 210. Programming for Data Analysis and Visualisation. This course will introduce students to data analysis and visualization with R. As an introduction to programming course, everyone is welcome. By the end of the course students will be able to gain insight from data, reproducibly (with literate programming and version control) and collaboratively, using modern programming tools and techniques.
This course comes from the datasciencebox.org project which is released under a Creative Commons Attribution Share Alike 4.0 International license.
- Week 1: Welcome to data science!
- Week 1: Meet the Toolkit R and RStudio
- Week 1: Meet the Toolkit Git
- Week 3: Tidy Data
- Week 3: Wrangle
- Week 3: Single dplyr verbs
- Week 3: Multi dplyr verbs
- Week 3: Tidying
- Week 4: Homework 02, Airbnb Edinburgh
- Week 4: Lab 03, Nobel Laureates
- Week 4: Application exercise
- Week 4: Application exercise
- Week 5: Effective Dataviz
- Week 5: Studies Confounding
- Week 5: Simpsons Paradox
- Week 5: Doing Data Science
- Week 5: Going Further with Data Viz
- Week 6: Homework 04, College Majors
- Week 6: Lab 05, UoE art
- Week 6: Application exercise
- Week 6: Homework 05, lego sales
- Week 7: Homework 04, money in politics
- Week 7: Lab 05, La Quinta is Spanish for Next to Denny's
- Week 7: Application exercise
- Week 7: Missing Data
- Week 8: Language of models
- Week 8: Fitting and interpreting models
- Week 8: Modelling nonlinear relationships
- Week 8: Models with multiple predictors
- Week 8: More models with multiple predictors
- Week 8: Homework 03
- Week 8: Lab 06, course evaluations
- Week 8: Lab 06, course evaluations
- Week 8: Lab 07, simpson's paradox
- Week 8: Lab 11, mlr course evaluations
- Week 8: Application exercise
- Week 9: Lab 07, work on projects
- Week 9: Application exercise
- Week 9: Lab GSS
- Week 9: Axes and Annotation
- Week 10: Cross Validation
- Week 10: Quantifying Uncertainty
- Week 10: Bootstrapping
- Week 10: Lab GSS 2
- Week 11: Text analysis
- Week 11: Comparing texts
- Week 11: Interactive web apps
- Week 11: Machine Learning
Data Science in a Box contains the materials required to teach (or learn from) an introductory data science course using R, all of which are freely-available and open-source. They include course materials such as slide decks, homework assignments, guided labs, sample exams, a final project assignment, as well as materials for instructors such as pedagogical tips, information on computing infrastructure, technology stack, and course logistics.
See datasciencebox.org for everything you need to know about the project!
Note that all materials are released with Creative Commons Attribution Share Alike 4.0 International license.
You can file an issue to get help, report a bug, or make a feature request.
Before opening a new issue, be sure to search issues and pull requests to make sure the bug hasn't been reported and/or already fixed in the development version.
By default, the search will be pre-populated with is:issue is:open.
You can edit the qualifiers (e.g. is:pr, is:closed) as needed.
For example, you'd simply remove is:open to search all issues in the repo, open or closed.
If your issue involves R code, please make a minimal reproducible example using the reprex package. If you haven't heard of or used reprex before, you're in for a treat! Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it'll take you to learn what it's all about). For additional reprex pointers, check out the Get help! section of the tidyverse site.
Please note that the datascience-box project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. Test line