By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. One of the last steps in the data analysis process is analyzing and manipulating the data.
You can take the entire series or individual courses that interest you. Given the diversity in educational background of our students we have divided the series into seven parts. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. These values are useful when creating groups or bins to organize larger sets of data. The mean, median and mode are all estimates of where the 'middle' of a set of data is. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. When performing statistical analysis on a set of data, the mean, median, mode, and standard deviation are all helpful values to calculate. We will use visualization techniques to explore new data sets and determine the most appropriate approach. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We provide R programming examples in a way that will help make the connection between concepts and implementation.
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R.
0 kommentar(er)
