Computer Science

This course discusses topics including database design with ERM/ERDD; theoretical foundations of relational database systems: relational algebra, functional dependencies, and normal forms; relational database development: SQL data definitions, foreign keys and other integrity constraints, and SQL as applicable language: essential language elements, and embedding in programming language; application programming, and object-relational mapping; security and protection concepts; transaction subject, transactional guaranties, synchronization of multi user operations, and fault tolerance features; and application and new developments: data warehousing, data mining, and OLAP. The topics are deepened in an implementation project for student groups.

This course provides an introduction to basic concepts of artificial intelligence. The course addresses heuristic search algorithm and knowledge reasoning of symbolic AI (Artificial Intelligence), a traditional AI approach. In addition, the course addresses theories about computational AI, such as genetic algorithms, and neural network learning, and how they can be applied in each field. The course also studies the application fields of artificial intelligence technologies.

This course introduces data processing in detail from the aspects of data calculation, sorting, query, screening, statistical summary, and chart generation, combined with some VBA programming practice. It improves students' data processing ability and enables non computer majors to learn the thinking and methods of computer programming.

Computational Thinking is a process of solving problems typically with four steps—decomposition, pattern recognition, abstraction, and algorithmic thinking. This course concentrates on algorithmic thinking and examines how to reformulate problems with step-by-step procedures to solve the problems. Students then practice the implementation of the procedures with Python programming language in their homework assignments. This course also covers various paradigms in designing the procedures such as divide-and-conquer, greedy methods, dynamic programming, backtracking, branch-and-bound, etc., along with fundamental data structures such as linked-lists, stacks, queues, recursion, graphs, trees, binary heaps, and hashing.