|
Choose one of these Laboratory sections:
|
|
12894
|
005
|
LAB
|
0
|
34
|
33
|
1
|
F
|
1:30-3:20pm
|
Aug19-Dec07
|
SC 179
|
Pascuzzi, PE |
Students will learn R to acquire, clean, explore and analyze biological data sets. Lectures and example data sets will show how data are linked to biological phenomena through human observation or instrumentation with inherent limitations. Students will learn how to organize data sets to optimize clarity and analytic possibilities while minimizing errors with examples drawn from the literature or biological databases. R programming will be taught starting with small-scale data such as drug sensitivity assays, qPCR, and metabolomics, moving to genome-scale analyses such as gene expression and pathway analysis later in the course. These skills will be taught in the light of enabling reproducible research through clear documentation of data sets and analyses. Relevant concepts from statistics will be reviewed, but it is assumed that students are familiar with basic statistical analyses.”
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And choose one of these Lecture sections:
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|
12883
|
004
|
LEC
|
3
|
34
|
33
|
1
|
W
|
1:30-3:20pm
|
Aug19-Dec07
|
SC 179
|
Pascuzzi, PE |
Students will learn R to acquire, clean, explore and analyze biological data sets. Lectures and example data sets will show how data are linked to biological phenomena through human observation or instrumentation with inherent limitations. Students will learn how to organize data sets to optimize clarity and analytic possibilities while minimizing errors with examples drawn from the literature or biological databases. R programming will be taught starting with small-scale data such as drug sensitivity assays, qPCR, and metabolomics, moving to genome-scale analyses such as gene expression and pathway analysis later in the course. These skills will be taught in the light of enabling reproducible research through clear documentation of data sets and analyses. Relevant concepts from statistics will be reviewed, but it is assumed that students are familiar with basic statistical analyses.”
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