STATISTICAL LEARNING IN HIGH-DIMENSIONAL DATA WITH OMICS APPLICATIONS [Archived Catalog]

2020-2021 Graduate & Professional Studies Catalog

BIOST 2078 - STATISTICAL LEARNING IN HIGH-DIMENSIONAL DATA WITH OMICS APPLICATIONS

Minimum Credits: 2
Maximum Credits: 2
This 2-credit course is a graduate level course to introduce theories and algorithms for statistical analysis of high-throughput genomic data. Emphases will be given to high-dimensional data analysis and theories behind the commonly used methods. This course is designed for graduate students who already have sufficient statistical background, have basic knowledge of various high-throughput genomic experiments (e.g. already finished BIOST 2055 or MSCBIO 2070) and wish to learn advanced statistical theories for bioinformatics and genomics research. Students are expected to have programming experiences in R (e.g. BIOST 2094) or in other low-level languages such as C, C++, Java and Fortran. The course will meet four hours per week for half a semester.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PREQ: BIOST 2039 and 2043; PLAN: Biostatistics (MS, PHD )

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