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An introduction to computer skills needed for the statistical sciences, using the software R. Covers reproducible research, data wrangling, visualisation, exploratory data analysis, resampling and simulation.
|Paper title||Visualisation and Modelling in R|
|Teaching period||Semester 2 (On campus)|
|Domestic Tuition Fees (NZD)||$913.95|
|International Tuition Fees (NZD)||$4,073.40|
- (BSNS 102 or BSNS 112 or STAT 110 or STAT 115) and 54 additional points
- STAT 380
- Schedule C
- Arts and Music, Science
- Teaching staff
To be confirmed, contact department for further information firstname.lastname@example.org
To be confirmed
- Graduate Attributes Emphasised
- Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy,
View more information about Otago's graduate attributes.
- Learning Outcomes
Fundamental to modern statistical practice is proficiency in the use of specialised software packages. This paper introduces students to the world of statistical computing, which encompasses fundamental programming skills motivated by handling and manipulating data, and how this relates to exploratory data analysis, visualisation, model fitting, and numerical simulation. Focus is on implementation in the R language and associated markup tools. Many of the skills students learn are transportable to other statistics packages.
Upon successful completion of the paper, the student will possess a range of skills used in and motivated by modern data analysis. They will be able to:
- Use R programming syntax and control flow
- Use R to read in, manipulate, tidy, subset, recode, and write out data sets
- Create, interpret and customise common statistical plots
- Use R to fit and interpret some common statistical models
- Conduct simulation of data and execute numerically intensive operations
- Write dynamic documents that include executable code