Bioinformatics is concerned with the study of the inherent structure of biological information, and statistical methods are the workhorses in many of these studies. Statistical Bioinformatics acknowledges the inherent variation found in data that are generated as part of the bioinformatics investigation and attempts to utilize experimental structure and design to partition variation in biological and technical components.

This course introduces the statistical methods commonly used in bioinformatics and biological research. Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. Students will learn the principles behind statistical methods and how they can be applied to analyse biological sequences and data.

There has been a great explosion of biological data and information in recent years, largely due to the advances in various high-throughput biotechnologies such as mass spectrometry, high throughput sequencing, and many genome-wide SNP profiling, RNA gene expression microarray, protein mass spectrometry, and many other recent high-throughput biotechniques. We can tackle all big data with statistics with different software like R, SPSS, Minitab, SAS and MATLAB. Statistical theory or methods to be introduced in this course include Z-test, t-test, regression, ANOVA, hypothesis testing and multivariate data analysis. Its 2nd course (Statistics for Biinfromatic II) will be available soon for advanced statistics.