COURSE DESCRIPTION: This course will cover the statistical principles that are pertinent to the study of big–omic data sets being collected in biology. Students will learn about current statistical approaches, issues related to experimental design and reproducible research, and important case studies that illuminate some of the challenges of analyzing big data. This course is the third module of the Quantitative Skills for the Biomedical Researcher series, and builds upon the material covered in the first two modules. As part of the assessment, students will gain practical experience by conducting a mini big data research project while working in small teams.

COURSE OBJECTIVES: Students will be taught practical skills to conduct big data analysis and understand the challenges/limitations of this field.

RECOMMENDED MATERIALS: Principles of Biostatistics (Second edition) by Marcello Pagano and Kimberlee Gauvreau, ISBN-13: 978-0534229023, ISBN-10: 0534229026; Laptop with R freeware installed.

PREREQUISITES:It is expected that students will have completed Quantitative Skills for the Biomedical Researcher I and II, or have acquired this material through other means (please consult the course leader if in doubt). Programming skills in R is mandatory. All students are expected to have a working knowledge of basic computers and college mathematics.

SUITABLE FOR 1ST YEAR STUDENTS: Not recommended; permission from course leader required if seeking to take this course in the first year.

STUDENT ASSESSMENTS: Final project (100%).

CREDIT HOURS: 1.0