Computer Science

This course offers a study of computer structures. Topics include: input/output, processor design; cache memory; virtual memory; IEEE754.

This course explores software project development. Topics include: functional design of the project; prototyping; user analysis; Android Studio; teamwork tools like Github and Trello; app development for Android.

Informatization is a historical process that makes full use of information technology, develops and utilizes information resources, promotes information exchange and knowledge sharing, and promotes the development and transformation of human society. This course strives to integrate the concepts, characteristics and development history of informatization into classroom teaching, cultivate students' informatization thinking, inspire inspiration for informatization innovation, promote students' thinking and practice, and develop innovative spirit, entrepreneurial awareness and innovation and entrepreneurship capabilities.

Information thinking is creative thinking that uses an information perspective to understand, analyze, and process things. It is a way of thinking that examines things from the aspects of information collection, transmission, storage, processing, sharing, and utilization. Informatization includes not only information technology means, but also changes in management and services. This course first introduces the basic concepts and basic characteristics of information thinking, then introduces the design and implementation of information systems and the changes they bring to management and services, and organizes students to think and practice the information applications around them inside and outside the classroom.

The course aims to make students understand the development history of database technology and the current status of database technology and products in China. It familiarizes students with the architecture and basic concepts of database systems, and provides them with the basic principles and methods of database design. Students are expected to have the ability to independently design database schemas and develop database applications.

Basic Content Overview: The course covers the following topics:

1. Development history of database technology and typical database products.
2. Relational model and database query language, including the basic structure of a relational database, relational algebra, tuple relational calculus, domain relational calculus, and the basic syntax and common usage of SQL.
3. Basictheoriesandmethodsofdatabasedesign,includingtheentity-relationship model, database design normalization theory, functional dependencies, and their applications.
4. Practical issues in database design, including design case analysis, database  optimization, storage structure and indexing, and transaction management.

This course introduces students to the core ethics concepts needed to build better technology and reason about its impact on the economy, civil society, and government. In the first half of the course, students consider ethical questions raised by different steps in the data science pipeline, such as: What is data, and how can we design better (ethical?) data governance regimes? Can technology discriminate? If so, what are promising strategies for promoting fairness and mitigating algorithmic bias? Can we understand black-box AI systems and explain their decisions? Why is it morally important that we do so? In the second half of the class, students consider ethical questions raised by the use of AI systems to manage our work, political, and social lives.

This course provides a study of how to build Python programs using fundamental programming structures like variables, conditional logic, looping, and functions. It looks at how to process data using lists, tuples, and dictionaries in Python programs; and how to read and write files in Python for data analysis. It explores problem-solving procedures, data representations, and algorithms through computers, and teaches practical programming techniques that can express the problem-solving process in Python programming language.

This course focuses on the methods and techniques for efficient management (modelling, manipulation, and retrieval) of data and information. It provides a foundation for later courses in database management and advanced information management. Students describe and use UML for information modeling; describe and use XML techniques for data modeling and querying; describe techniques for exposing and retrieving information on the web semantic web/linked data approaches; and understand the ongoing collaborative process of eliciting ethical implications which influence technology design.

Modern information science has given rise to techniques for acquiring and analyzing a variety of observable data in order to reveal multimodal characteristics of humans. The objective of this course is to study sequential data analysis, machine learning, relation to cognitive science, and its applications through information processing represented by natural language processing, EEG, and speech.