Biol20N02 2016: Difference between revisions

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==Course Description==
==Course Description==
Molecular evolution is the study of the change of DNA and protein sequences through time. Theories and techniques of molecular evolution are widely used in species classification, biodiversity studies, comparative genomics, and molecular epidemiology. Contents of the course include:
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.  
* Population genetics, which is a theoretical framework for understanding mechanisms of sequence evolution through mutation, recombination, gene duplication, genetic drift, and natural selection.
* Molecular systematics, which introduces statistical models of sequence evolution and methods for reconstructing species phylogeny.
* Bioinformatics, which  provides hands-on training on data acquisition and the use of software tools for phylogenetic analyses.


This 3-credit course is designed for upper-level biology-major undergraduates. Hunter pre-requisites are BIOL203, and MATH150 or STAT113.
This 3-credit experimental course fulfills elective requirements for Biology Major I. Hunter pre-requisites are BIOL100, BIOL102 and STAT113.
 
<font color='red'>Please note that starting from fall 2015, completing this course no longer counts towards research credits for biology majors.</font>


==Textbooks==
==Textbooks==

Revision as of 16:18, 6 December 2015

Analysis of Biological Data (BIOL 20N02, Spring 2015)
Instructor: Dr Weigang Qiu, Associate Professor, Department of Biological Sciences
Room: 1001B HN (North Building, 10th Floor, Mac Computer Lab)
Hours: Tuesdays 10-1
Office Hours: Belfer Research Building (Google Map) BB-402; Wed 5-7 pm or by appointment
Course Website: http://diverge.hunter.cuny.edu/labwiki/Biol20N2_2016
Borreliabase-screenshot-1.png

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

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