Probability is a mathematical science including methods of collecting, organizing and analyzing data in such a way that meaningful conclusions can be drawn from them. The data are presented in the form of tables and graphs. The characteristics of the data are described in simple terms.

Introduction: Probability distributions are mathematical models to depict the spread of data using parameters. Some well known ones include the Binomial, Normal, Poisson and Beta distributions. Modeling data from these distributions allows us to uncover the parameters and use some assumptions to perform statistical hypothesis tests. Essential descriptive statistics are reviewed and then used in various situations to calculate background, noise, normalization and thresholding. Data visualization using various graphs will also be reviewed. Armed with these techniques, students will be able to better deal with the challenges of data analysis and understand data at a more fundamental level.


Why learn about Distributions? students will be able to better deal with the challenges of data analysis and understand data at a more fundamental level


Logistics: This online will be conducted in R, a free and open-source package for statistical computing that has become an essential part of Bioinformatics. This course focuses on the mathematical principles of statistical analysis and not on the syntax and functionality of R. The course is divided into 4 sessions. No experience is required, although prior experience with a programming language will be helpful. Math skills will also be useful.


Price:$1200 for Commercial/Government enrollees and $900 for Academic researchers and students.


Instructor: Shailender Nagpal


Syllabus:

  • Probability distributions and how to work with them
  • Descriptive statistics for summarizing vector and matrix data
  • Student t-tests, Wilcoxon tests for analyzing one and two sample data
  • Use of the techniques covered in the previous sessions to do a biological data analysis project