The main aim of this course is to teach you to how to approach data analysis problems
with classical statistics. We focus on the intuition behind statistical methodologies
rather than on “how to run a t-test with R” (which we will also learn, by the way).
First we review the foundations (sampling theory, discrete and continuous distributions),
then we continue with hypothesis testing. The technology itself is introduced using “Student”‘s t-test
as an example, with a strong emphasis on errors (false positives, p-value distributions,
test power calculations). Finally a short “cookbook of tests” is offered.
Instructor: András Aszódi.
This course teaches the same statistical concepts as the Basic statistics with Python
training but uses the R programming language.
We cannot go into the specific data analysis problems of your particular project.
Furthermore, this course will not teach you bioinformatics.
In particular, no high-throughput sequencing data will be used because they are impractically large,
and not everyone on campus is working with sequencing.
Basic familiarity with R is required. In particular the following skills are necessary:
If you have attended our R as a programming language training
then you are well equipped to take this course.
You can bring your own data to this course and run
a “Student”‘s t-test on it.
a comma-separated-values (CSV) file with UNIX line endings (n) that
contains two columns corresponding to the two groups of data whose means you would
like to compare. The size of the two groups need not be the same.
Number of participants: minimum 5, maximum 10.
Length: The course takes two half-days,
from 09:00 to 13:00 with 2 breaks.
Please provide affiliated Organisation, and your use-case or resource use estimation (CPU-hours, GPU-hours, Memory)