Overview
Uses real biological examples and computers, and deals with types of data and their acquisition; graphical and exploratory analysis; estimation and hypothesis testing; experimental design; computer-intensive methods and simulation.
This paper covers experimental design and data analysis techniques widely used in the biological sciences, taught using the free software R.
About this paper
| Paper title | Biological Data Analysis and Computing |
|---|---|
| Subject | Zoology |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,318.20 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (STAT 110 or STAT 115) and 54 200-level points from Science Schedule C
- Restriction
- WILM 404
- Schedule C
- Science
- Contact
- zoology@otago.ac.nz
- Teaching staff
Professor Christoph Matthaei (Course Coordinator)
- Paper Structure
- 24 lectures (30-45 minutes), with 24 corresponding tutorials (60-75 minutes) involving hands-on programming and data analysis using R, which are taught in computer labs (assisted by student demonstrators).
- Teaching Arrangements
- The first course module (3 lecture/tutorial sessions) provides an in-depth training in experimental design.
The second course module (9 sessions in total) covers fundamental statistical issues (4 sessions), simple analyses (2 sessions) and complex analyses (3 sessions).
Modules 3 and 4 (12 sessions in total) cover mainly intermediate and advanced techniques complementing Module 2. - Textbooks
- Quinn, G.P. and Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge, UK.
Whitlock, M.C. and Schluter, D. (2009) The Analysis of Biological Data. Roberts & Co. Publishers, Colorado, USA.
Kruschke, J.K. (2011) Doing Bayesian Data Analysis: A tutorial with R and BUGS. Elsevier. - Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes. - Learning Outcomes
- Students will gain an understanding of key issues related to experimental design and data analysis. They will also learn to use the free software R to conduct a range of analyses (from basic to complex).
Specific aims of the paper include:- Helping you design field or laboratory experiments
- Helping you gather, present and interpret biological data
- Enabling you to make recommendations and decisions about biological systems
- Giving you a foundation for understanding, critically evaluating and using statistical data
- Building up your knowledge slowly and thoroughly
- Hopefully, helping reduce a possible fear or dislike of stats!
Overview
Uses real biological examples and computers, and deals with types of data and their acquisition; graphical and exploratory analysis; estimation and hypothesis testing; experimental design; computer-intensive methods and simulation.
This paper covers experimental design and data analysis techniques widely used in the biological sciences, taught using the free software R.
About this paper
| Paper title | Biological Data Analysis and Computing |
|---|---|
| Subject | Zoology |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (STAT 110 or STAT 115) and 54 200-level points from Science Schedule C
- Restriction
- WILM 404
- Schedule C
- Science
- Contact
- zoology@otago.ac.nz
- Teaching staff
Course Co-ordinator: Professor Christoph Matthaei
- Paper Structure
Twenty-three lectures (30-45 minutes), with 23 corresponding tutorials (60-75 minutes) involving hands-on programming and data analysis using R, which are taught in computer labs (assisted by student demonstrators). The final session is a course review/exam preview.
- Teaching Arrangements
The first course module (3 lecture/tutorial sessions) provides an in-depth training in Experimental Design.
The second course module (8 sessions in total) covers fundamental statistical issues (3 sessions), simple analyses (2 sessions) and complex analyses (3 sessions).
Modules 3 and 4 (12 sessions in total) cover mainly intermediate and advanced techniques complementing Module 2.
- Textbooks
- Quinn, G.P. and Keough, M.J. (2002) Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge, UK.
- Whitlock, M.C. and Schluter, D. (2009) The Analysis of Biological Data. Roberts & Co. Publishers, Colorado, USA.
- Kruschke, J.K. (2011) Doing Bayesian Data Analysis: A tutorial with R and BUGS. Elsevier.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes. - Learning Outcomes
Students will gain an understanding of key issues related to experimental design and data analysis. They will also learn to use the free software R to conduct a range of analyses (from basic to complex).
Specific aims of the paper include:
- Helping you design field or laboratory experiments
- Helping you gather, present and interpret biological data
- Enabling you to make recommendations and decisions about biological systems
- Giving you a foundation for understanding, critically evaluating and using statistical data
- Building up your knowledge slowly and thoroughly
- Hopefully, helping reduce a possible fear or dislike of stats!