MED5020: Biomedical Omics Data Analysis
Since the advent of the human genome sequences in 2001, related omics technologies have developed rapidly. Facing the huge data obtained by sequencing, how to effectively analyze them is a problem that many researchers often encounter in daily research. Biomedical Omics Data Analysis focuses on the applications of omics technologies in biomedical research. This course also emphasizes hands-on experience with the aim to teach students the basic omics data analysis workflow. After completing this course, students are expected to master the basic Unix environment and R language programming skills, and can use Shell and Bioconductor to process common omics data encountered in their thesis research (whole genome/exome sequencing, RNA-Seq, Single-cell RNA-Seq, ChIP-Seq, ATAC-Seq and long-read sequencing etc.); or can better understand their analysis needs, so that they can communicate with bioinformaticians more efficiently. In addition, this course also aims to teach students the general principles behind common data analysis methods without getting into too many algorithmic details, so that students can understand the reasoning and logics behind each analysis step. Finally, this course also hopes to teach students how to better design omics experiments to make subsequent analysis easier and make conclusions more reliable.
CS112: Introduction to Python Programming
This course introduces the basic concepts of Python programming language and the corresponding programming skills. The main contents include the Python programming environment setup, main components of Python (variables, operators, data type, etc.), flow control, functions, lists, dictionaries, tuples and sets, input and output, plotting, Numpy, SciPy, Pandas, and objected-oriented programming. Upon finishing the course, the students are expecting to use Python language to solve basic scientific computing problems fluently and efficiently.