## lectures

This section contains the lectures
given during the course. On the left
are links to all of the lecture
slides.

**Introduction to
R
[2x2]
[source]**
In this lecture we will cover the
programming language R. We will cover
topics such as syntax and semantics,
data structures. In this lecture we
will set up our programming
environment and work through a number
of basic problems which will occurr
frequently in the context of R
programming. This lecture will
introduce a majority of the components
necessary to effectively program in R.

**Introduction to
Bioconductor
[2x2] [source]**
In this lecture we will introduce
Bioconductor. This will be necessary
for a number of examples in the future
lectures. This lecture will not be an
exhaustive presentation of
Bioconductor, but rather a short look
at some of the base classes and the
data sets available.

**Introduction to Statistical Simulation
[2x2] [source]**
In this lecture we will cover the basics of
statistical simulation. We will investigate the
law of large numbers, central limit theorem, and
other issues regarding normality.

**Programming in R
[2x2] [source]**
In this lecture we will cover more of R. We will cover in
depth functions and scope and some of the semantics of
R. This lecture will also talk about classes and
debugging.

**Exploratory Data Analysis
[2x2] [source]**
In this lecture we will examine data visualization
extensively. We will cover the plotting functions
available in R and when to use them. We will cover
interpreting and constructing complicated plots.

**Hypothesis Testing
[2x2] [source]**
In this lecture we will discuss p-values, hypothesis
testing two-sample tests. We will discuss both parametric
and non-parametric tests.

**Case Study: Smith et. al.
[2x2] [source]**
In this lecture we will discuss the recently published
Smith et. al. paper from PLoS biology.