https://github.com/jamesquinlan/dsc130-datasci-r
Course repository for an introduction to data science with R.
https://github.com/jamesquinlan/dsc130-datasci-r
data-science r
Last synced: 28 days ago
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Course repository for an introduction to data science with R.
- Host: GitHub
- URL: https://github.com/jamesquinlan/dsc130-datasci-r
- Owner: jamesquinlan
- License: mit
- Created: 2022-08-27T16:33:42.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-29T19:17:49.000Z (10 months ago)
- Last Synced: 2025-03-29T10:53:41.704Z (about 1 month ago)
- Topics: data-science, r
- Homepage:
- Size: 4.51 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introduction to Data Science with R
## Course description
This course is an introduction to data science using the R programming language with a focus on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modeling, and effective communication of results. Students will create visualizations and models, and use them to gain insights and make predictions. In this model-based course, students are introduced to statistical analysis and models through examples. Students will perform meaningful analysis on real data. This course assumes no prior experience with programming and requires no specific statistical or mathematical knowledge beyond high school algebra.
## Open Resources
Resources below are free electronically.
- [OpenIntro Statistics](https://leanpub.com/openintro-statistics)
- [R for Data Science](https://r4ds.had.co.nz/), by Grolemund and Wickham.
- [R Cookbook](https://rc2e.com/), by Long and Teetor.
- [Hands on Programming with R](https://rstudio-education.github.io/hopr/) by Grolemund
## Learning Outcomes
After successful completion of this course the student will be able to:
- Acquire and wrangle data;
- Use effective and appropriate methods for visualizing and describing data;
- Demonstrate "best practice" coding;
- Demonstrate proficiency building basic statistical models, test hypothesis, and use models for interpretation and prediction; and
- Communicate results (via interactive dashboard, i.e., RShiny).