Overview
Topics include an overview of genetics and molecular biology; genetic, genomic, and proteomic technologies; analysis of large data sets; incorporation of biological information into the statistical analysis process.
The analysis of large data sets is becoming increasingly important in many areas. The techniques covered in this paper will be applicable to a wide range of data types, including non-biological data. Exposure to other disciplines (in this case biomedical science) is a must for any applied statistician. Interacting with students from other fields is stimulating and will help you appreciate your statistical skills.
About this paper
| Paper title | Data Analysis for Bioinformatics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,393.95 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Eligibility
- This paper is open to fourth-year students from the Biological and Medical Sciences, Mathematics and Statistics, and Computer Science. As long as you have skills in one of these areas, any remaining gaps will be filled in during the paper. Experience with R will certainly help.
Enrolments for this paper require departmental permission. View more information about departmental permission. - Contact
- Teaching staff
Professor Mik Black (Department of Biochemistry)
- Paper Structure
- Main topics:
- Overview of genetics and molecular biology
- Introduction to genomics technologies
- Methods for the statistical analysis of large data sets
- Application of standard statistical methods
- Introduction to new purpose-built methods
- Incorporation of biological information into the statistical analysis process
- Teaching Arrangements
- One 2-hour session per week.
- Textbooks
- Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Communication, Critical thinking.
View more information about Otago's graduate attributes. - Learning Outcomes
- Students who successfully complete the paper will demonstrate in-depth understanding of the central concepts and theories.
- Assessment details
Three assignments (each worth 15%): 45%
Final exam (closed book, three hours): 55%
Overview
Topics include an overview of genetics and molecular biology; genetic, genomic, and proteomic technologies; analysis of large data sets; incorporation of biological information into the statistical analysis process.
The analysis of large data sets is becoming increasingly important in many areas. The techniques covered in this paper will be applicable to a wide range of data types, including non-biological data. Exposure to other disciplines (in this case biomedical science) is a must for any applied statistician. Interacting with students from other fields is stimulating and will help you appreciate your statistical skills.
About this paper
| Paper title | Data Analysis for Bioinformatics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 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. |
- Eligibility
- This paper is open to fourth-year students from the Biological and Medical Sciences, Mathematics and Statistics, and Computer Science. As long as you have skills in one of these areas, any remaining gaps will be filled in during the paper. Experience with R will certainly help.
Enrolments for this paper require departmental permission. View more information about departmental permission. - Contact
- Teaching staff
Professor Mik Black - Department of Biochemistry
- Paper Structure
Main topics:
- Overview of genetics and molecular biology
- Introduction to genomics technologies
- Methods for the statistical analysis of large data sets
- Application of standard statistical methods
- Introduction to new purpose-built methods
- Incorporation of biological information into the statistical analysis process
- Teaching Arrangements
- One 2-hour session per week.
- Textbooks
- Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Communication, Critical thinking.
View more information about Otago's graduate attributes. - Learning Outcomes
- Students who successfully complete the paper will demonstrate in-depth understanding of the central concepts and theories.
- Assessment details
- Three Assignments - each worth 15% (45%)
- Final Exam - closed book, three hours (55%)