teaching.html

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<h1> ISU Classes </h1>
<ul>
  <li>	<h3>Stat 201 - Introduction to Statistics (Honors) </h3>
		<p>
		Introductory statistics for honors level undergraduate students. The objectives of the course are to help students develop an understanding of statistical thinking and to enable students to apply basic statistical techniques. By the end of the course students should be informed and critical consumers of quantitative arguments.
		</p>
		<p>
		Spring 2014
		</p> </li>
  <li> <h3>Stat/Engl 332 - Visual Communication of Quantitative Information </h3>
		<p>
		This course will help prepare students to be active citizens in the information technology age. Students will develop critical thinking skills about how information is visually presented, and they will learn how to accurately and attractively communicate quantitative information using graphics. At the end of the course students will: (1) know about important historical and contemporary examples, (2) know about and how to implement the elements of graphical design, (3) be able to evaluate visual presentations of information in the media, and, (4) be able to use the computer to generate graphics to communicate information effectively
		</p>
		<p>
		Fall 2012
		</p> </li>
	  	<li> <h3> <a href="http://streaming.stat.iastate.edu/~dicook/multivariatelectures/">Stat 407 - Methods of Multivariate Analysis</a> </h3>
		<p>
		The objectives of the course are to help students: (1) Grasp the concepts and develop critical thinking in multivariate statistical analysis. (2) Learn about multivariate problems. (3) Compute analyses using standard statistical software. (4) Learn suffcient vocabulary to read further about new methodology. (5) Apply the methodology to new problems.
		</p>
		<p>
		Fall 2014
		</p> </li>
		<li> <h3> <a href="http://streaming.stat.iastate.edu/~dicook/EDA.and.datamining/">Stat 503 - Exploratory Methods and Data Mining</a> </h3>
		<p>
		Approaches to finding the unexpected in data: data mining, pattern recognition and understanding. Emphasis is on data-centered, non-inferential statistics, for large or high-dimensional data, and topical problems. Simple graphical methods, as well as classical and computer-intensive methods applied in an exploratory manner.		
		</p>
		<p>
		Spring 2013
		</p> </li>
		<li> <h3> <a href="stat585/index.html">Stat 585 - Data Technologies for Statistical Analysis</a> </h3>
		<p>

		Introduction to statistical computing for data analysis. Reading and working with dfferent data formats: flat files, databases, web technologies, netCDF. Working with massive data, handling missing data. ffcient programming, reproducible code. Primarily using R but other software SAS, unix commands, awk, python as needed to conduct data analysis. Topical real data problems.
		</p>
		<p>
		Spring 2014
		</p> </li>
</ul>