https://github.com/secary/maths7107
Data Taming
https://github.com/secary/maths7107
r rstudio statistics
Last synced: 11 months ago
JSON representation
Data Taming
- Host: GitHub
- URL: https://github.com/secary/maths7107
- Owner: secary
- Created: 2024-10-11T05:40:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-17T09:08:22.000Z (over 1 year ago)
- Last Synced: 2025-07-04T21:07:23.976Z (12 months ago)
- Topics: r, rstudio, statistics
- Language: R
- Homepage: https://www.adelaide.edu.au/course-outlines/111085/1/tri-2/
- Size: 34.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MATHS 7107, Tri2, 2024
## [Data Taming](https://www.adelaide.edu.au/course-outlines/111085/1/tri-2/)
This course is a practical introduction to the practice of wrangling, finding relationships in, and making predictions from, messy datasets using statistical methods. The course introduces the principles of tidy data, types of data and data formats, exploratory data analysis, data transformation, as well as model fitting and prediction using statistical machine learning tools. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.
## Module 1
Introduction to R & data frames
## Module 2
Data cleaning & text manipulation
## Module 3
Reproducible research & Rmarkdown
## Module 4
Summarising data & interpreting plots
## Module 5
Transforming data
## Module 6
Predicting with straight lines (Linear regression)
## Module 7
Predicting with lots of straight lines (Multiple regression)
## Module 8
Classification & Cross-Validation
## Module 9
Predicting with curved lines/classification
## Module 10
Predicting with fancy lines
## Module 11
Case studies
## Module 12
Case studies, revision