Biol20N02 2016: Difference between revisions
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==Course Outline== | ==Course Outline== | ||
Week 1. Introduction & tutorials for R/R studio | ===Week 1. Introduction & tutorials for R/R studio | ||
Week 2. Statistics & samples | ===Week 2. Statistics & samples | ||
Week 3. Displaying data | ===Week 3. Displaying data | ||
Week 4. Describing data; Exam 1. | ===Week 4. Describing data; Exam 1. | ||
Week 5. Probability and hypothesis testing | ===Week 5. Probability and hypothesis testing | ||
Week 6. Analysis of proportions | ===Week 6. Analysis of proportions | ||
Week 7. Analysis of frequencies | ===Week 7. Analysis of frequencies | ||
Week 8. Contingency tests; Exam 2 | ===Week 8. Contingency tests; Exam 2 | ||
Week 9. Normal distribution and controls | ===Week 9. Normal distribution and controls | ||
Week 10. Comparing two means | ===Week 10. Comparing two means | ||
Week 11. Designing experiments | ===Week 11. Designing experiments | ||
Week 12. Comparing more than two groups; Exam 3 | ===Week 12. Comparing more than two groups; Exam 3 | ||
Week 13. Correlation analysis | ===Week 13. Correlation analysis | ||
Week 14. Regression analysis | ===Week 14. Regression analysis | ||
Week 15. Review and Exam 4 (final comprehensive exam) | ===Week 15. Review and Exam 4 (final comprehensive exam) | ||
Revision as of 16:27, 6 December 2015
Course Description
With rapid accumulation of genome sequences and digitalized health data, biomedicine is becoming a data-intensive science. This course is a hands-on, computer-based workshop on how to visualize and analyze large quantities of biological data. The course introduces R, a modern statistical computing language and platform. Students will learn to use R to make scatter plots, bar plots, box plots, and other commonly used data-visualization techniques. The course will review statistical methods including hypothesis testing, analysis of frequencies, and correlation analysis. Student will apply these methods to the analysis of genomic and health data such as whole-genome gene expressions and SNP (single-nucleotide polymorphism) frequencies.
This 3-credit experimental course fulfills elective requirements for Biology Major I. Hunter pre-requisites are BIOL100, BIOL102 and STAT113.
Textbooks
- R Studio (Required): Learning RStudio for R Statistical Computing
- Digital textbook (Required): Data Analysis for the Life Sciences
Course Outline
===Week 1. Introduction & tutorials for R/R studio ===Week 2. Statistics & samples ===Week 3. Displaying data ===Week 4. Describing data; Exam 1. ===Week 5. Probability and hypothesis testing ===Week 6. Analysis of proportions ===Week 7. Analysis of frequencies ===Week 8. Contingency tests; Exam 2 ===Week 9. Normal distribution and controls ===Week 10. Comparing two means ===Week 11. Designing experiments ===Week 12. Comparing more than two groups; Exam 3 ===Week 13. Correlation analysis ===Week 14. Regression analysis ===Week 15. Review and Exam 4 (final comprehensive exam)