Intro to Computational BioStatistics with R  (Fall 2018) (Old site; new site is at https://scinet.courses)

In this course data analysis techniques utilizing the R statistical language, will be discussed and introduced, as well as, the basics of programming and computational statistics (ie. application of statistical methods by using computers).
The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.

Structure

• Twelve weeks, with two 1-hour lectures per week: Tuesdays and Thursdays from 1pm until 2pm.
• Final grade will be based on weekly/bi-weekly assignments and one mid-term to be taken during one of the lectures.
• Passing mark: 70% of the combined final grade.

Topics include: R programming, version control, automation, modular programming and visualization.

Location of the lectures: Medical Sciences Building MSB4279

The lecturers will be computational science specialists from the SciNet High Performance Computing Consortium at the University of Toronto.

Prerequisites
Students should ideally have some light programming experience in any language, and a bit of command-line experience is a plus.
Students should have a laptop to bring to the lectures, with R installed, which is freely available for Linux, OS X and Microsoft Windows.

Topics to be covered:

• Introduction to programming with R. IDEs and R standard console.
• Programming best practises, functions, libraries, modular programming.
• Data structures (vectors, matrices, arrays, data frames).
• Software version control.
• Review of basic statistics using R (GLM, regression methods, cluster analysis, decision trees).
• Some more advanced topics in computational statistics.
• Introduction to Machine Learning algorithms.
• Binary file input/output.
• Visualization of data, publication-quality figures.
• Generation of online and interactive plots.
• Parallel techniques and distributed data analysis.

Course Syllabus

Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.