- Teacher: 明德 紀
This course aims to introduce data science from a pragmatic, practice-oriented viewpoint. Students will learn concepts, R programming language and tools they need to deal with various facets of data science practice, including data integration, exploratory data analysis, predictive modeling, evaluation and effective visual communication. By the end of the course they will be able to apply data science techniques on their own research topics.
This course is designed to provide a substantial exposure to the C programming language in a Unix environment for students with basic programming experience. Advanced features of the C language that are emphasized include structs and unions, File I/O, dynamic memory allocation (malloc and free), pointers, pointer arithmetic, and casting. Data structures that are emphasized include linked lists, queue, and stack. Students will develop a sense of proper programming style in C, and will be exposed to cross-platform portability issues. Students will also learn to use several Unix tools such as gdb, emacs/vi, svn/git, make, grep, diff etc. to assist them in the design, testing and debugging of their programs. In addition, students will learn some basic shell scripting, and Perl/Python to solve simple problems.