Overview
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.
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 mark-up tools. Many of the skills students learn are transportable to other statistics packages.
About this paper
| Paper title | Visualisation and Modelling in R |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $955.05 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (BSNS 102 or BSNS 112 or STAT 110 or STAT 115) and 54 additional points
- Restriction
- STAT 380
- Schedule C
- Arts and Music, Science
- Contact
- Teaching staff
Dr Tilman Davies
- Textbooks
To be confirmed
- Graduate Attributes Emphasised
- Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
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
Timetable
Overview
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.
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 mark-up tools. Many of the skills students learn are transportable to other statistics packages.
About this paper
| Paper title | Visualisation and Modelling in R |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $981.75 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (BSNS 102 or BSNS 112 or STAT 110 or STAT 115) and 54 additional points
- Restriction
- STAT 380
- Schedule C
- Arts and Music, Science
- Contact
- Teaching staff
Dr Tilman Davies
Dr Xun Xiao
- Textbooks
To be confirmed
- Graduate Attributes Emphasised
- Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
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