https://github.com/danilofreire/datascience-box

Data Science Course in a Box

https://github.com/danilofreire/datascience-box

Repository

Data Science Course in a Box

https://github.com/danilofreire/datascience-box/blob/master/

# Data Science Course in a Box 

## Course description

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 consistent 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.

## Contents

- `slides`: 26 `xaringan` slide 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
- (WIP) `tutorials`: Interactive learning exercises built with `learnr` 
- (WIP) `website`: This website includes links to all of the above and contains 
additional material for helping instructors set up their course.

## Resources

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.

### Talks

- SDSS 2018: [Start with Data Science](https://github.com/mine-cetinkaya-rundel/start-with-ds)
- useR 2017: Teaching data science to new useRs
  - [Slides](http://bit.ly/user2017)
  - [Video](https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/KEYNOTE-Teaching-data-science-to-new-useRs)

### Articles

- [Practical Data Science for Stats](https://peerj.com/collections/50-practicaldatascistats/) collection
- [Curriculum Guidelines for Undergraduate Programs in Data Science](https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-060116-053930)

### Teaching materials

- R4DS: http://r4ds.had.co.nz/
- RStudio Primers: https://rstudio.cloud/learn/primers

### Tools

- [ghclass](https://rundel.github.io/ghclass/)

## Attribution

If you plan on using any of the materials in this repository, please review the 
[license](LICENSE.md). Educators heavily re-using the materials are encouraged to 
add the following note to their course homepage / syllabus / repository: "Materials 
used in this course are derived from [datasciencebox.org](https://datasciencebox.org)."
If you are only using a small subset, please display a similar attribution message 
on the specific derived item.

## Feedback

We would love to hear from you if you are using these resources. 
Please take just a few minutes to fill out 
[this Google form](https://forms.gle/AjqXStdFq42TTV9LA). All fields are optional, 
but the more information you provide, the more data we will have to assess the 
reach and impact of this project.

Owner

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