For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| DSC2016 | Ethical and social impact of data | 3 | 6 | Major | Bachelor | 1-2 | Data Science | English | Yes |
| Data ethics refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data. This course covers major issues in ethical and social impact of data including topics such as ownership, privacy, and openness an other important topics. | |||||||||
| DSC2017 | Knowledge Graph | 3 | 6 | Major | Bachelor | 2-4 | Data Science | - | No |
| This course covers design, implementation, and applications of knowledge graph. Through successful global use cases of knowledge graph, the course aimed to teach students the importance of semantic data management. | |||||||||
| DSC3001 | Practice in Big Data Analytics | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| This course brings together several key information technologies used in manipulating, storing, and analyzing big data. We look at the basic tools for statistical analysis, R, and key methods used in machine learning. We touch on related tools that provide SQL-like access to unstructured data. We analyze so-called NoSQL storage solutions for their critical features: speed of reads and writes, data consistency, and ability to scale to extreme volumes. We examine memory resident databases and streaming technologies which allow analysis of data in real time. Students gain the ability to design highly scalable systems that can accept, store, and analyze large volumes of unstructured data. | |||||||||
| DSC3002 | Practice in Data Visualization | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| This course teaches information design fundamentals and introduces a variety of visualization tools and techniques. At the end of the course, the student will be able to identify which visualization technique are best suited to deliver high impact messages under a variety of situations. The student will also learn how to present meaningful information in the most compelling and consumable fashion. The course deals with charts and tables as main modes of data visualization. | |||||||||
| DSC3004 | Digital Humanities | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| Digital humanities refers to all new types of humanities research, education, and creative projects enabled by information technology. The definition is not limited to studies of traditional humanities topics using information technology as a research method; but it also includes completely new forms of humanities research realized by the use of computers. This course introduces students to current digital humanities projects as well as tools for approaching humanities research in new ways such as digital scholarly editing, the creation of thematic archives, programmatic analysis of large-scale textual corpora, data mining, visualization of research out etc. On completion of this course students will be able to become familiar and conversant with various concepts and methods in the digital humanities develop the critical thinking skills necessary to evaluate digital scholarship | |||||||||
| DSC3008 | Practice in Medical Information Systems | 3 | 6 | Major | Bachelor | Data Science | - | No | |
| This course discusses the key characteristics, standards, and management of medical and healthcare data, and current health information technology-related issues, in particular, mobile technology, bioinformatics, public health informatics, online medical resources and medical information retrieval, medical imaging informatics, and disease management and disease registries. In order to utilize medical and healthcare database systems, the course practices PubMed and CINAHL (Cumulative Index to Nursing and Allied Health Literature). | |||||||||
| DSC3009 | Principles and Practice in Data Mining | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| This course will provide the students with understanding of the fundamental data mining methodologies, and with the ability of formulating and solving problems with them. The particular attention will be paid to practical, efficient and statistically sound techniques. capable of providing not only the requested discoveries, but also the estimates of their utility. This class will be focused on hands-on experiences using data mining software. Students will have an opportunity to develop intuition needed to safely navigate through the complicated environment of today‘s data mining business market. | |||||||||
| DSC3011 | Applied Machine Learning | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| The course introduces core machine learning tasks, algorithms, and techniques that are widely used in real-world machine learning projects. Major machine learning tasks such as classification, clustering, and regression will be introduced with widely used algorithms for the tasks. In addition to the tasks and algorithms, techniques such as data preprocessing, dimensionality reduction, model evaluation that are important for implementing machine learning applications will be covered. Students will learn machine learning by practicing with Python-based open source machine learning packages. | |||||||||
| DSC3012 | Principles and Practice in Social Data | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| This course introduces the basic concepts of social media analysis methodology and covers practical skills needed in analyzing live social data. The main objective of this course is to provide students with solid grasp of social media data and ability to analyze them. During the practice session, students will learn how to develop ontology needed for effective and efficient processing of social data. Furthermore, this course introduces basic skills needed in using analysis software such as SAS Enterprise Content Categorizer. Students will also learn basic concepts of SAS ECC design and data management given topics to analyze. Based on analysis results of SAS ECC data, students will learn how to generate insight reports for general managers. | |||||||||
| DSC3013 | Introduction to Deep Learning | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| This course aims to provide basic knowledge and practical skills in deep learning. It starts with an introduction to neural networks, which is the basic building block for deep learning. Students will be exposed to the issues associated with building and training a deep neural network taking into consideration other facets such as fully connected layers, convolutional layers and recurrent layers. Major deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) will be discussed. Practical assignments in training deep neural networks using state-of-the-art frameworks (e.g. TensorFlow, PyTorch, Keras) will assist in grasping the technical skills required to be a deep learning data scientist. Students are assumed to have basic knowledge in programming (Python/R), linear algebra and calculus, and ideally machine learning. | |||||||||
| DSC3021 | Introduction to Artificial Intelligence | 3 | 6 | Major | Bachelor | Data Science | English | Yes | |
| This course aims to teach the fundamentals of modern artificial intelligence, such as the concepts of intelligence and intelligent agents for starters. Next, it will probe into problem solving, introducing the notion of search. Then, the basics of knowledge representation and reasoning, such as logic and planning will be explored. Machine learning, a fast growing subfield of AI will also be covered. Further topics would include, but not limited to, foundations of natural language processing, semantic web, robotics and computer vision, concluding the communication, perceiving and acting of a rational agent. This is an introductory course and would be suitable for anyone interested to get started in AI. Students will be given assignments with minimal or no programming required. | |||||||||
| DSC3024 | Location-based Data Analytics | 3 | 6 | Major | Bachelor | Data Science | - | No | |
| Students in this course will learn Geographic Information System(GIS) and its application. GIS is a tool for acquiring, storing, analyzing and processing of geographic data. Students will be exposed to a variety of GIS applications and methods to utilize location-based data. | |||||||||
| DSC3032 | Deep Learning 1: Foundations and Image Processing | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| This course aims to provide practical skills in deep learning and in particular, image processing. Deep learning is at the core of the AI revolution and data science. It starts with an introduction to neural networks (NNs) and tensors, which are the basic building blocks for deep learning. Using Python-based libraries and PyTorch framework in the Google Colaboratory environment, students will dive in with coding simple NNs to deep NNs to classify fashion items, recognise handwritten digits, and even distinguish cats from dogs from images. Deep learning architectures such as multi-layer perceptrons, convolutional neural networks (CNNs), VAEs and GANs for image processing will be explored. Practical assignments in training deep learning models using classic datasets (MNIST, CIFAR-10, etc.) and well-known models (Inception v3, AlexNet, etc.) will assist in grasping the technical skills required. Students are expected to have basic knowledge in Python and basics in AI/machine learning. | |||||||||
| DSC3033 | Deep Learning 2: Natural Language Processing | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| This course aims to provide practical skills in natural language processing (NLP). Natural language is used everywhere, not just in our everyday speech, but also in digital platforms in the form of textual data. NLP is a fast evolving field that is making waves today. Using the latest technologies, such as PyTorch and Hugging Face, students will first learn how textual data is preprocessed and prepared before being fed into deep learning models. Deep learning architectures for NLP such as recurrent neural networks (RNNs), Sequence2Sequence, and Transformers will be explored in detail. NLP tasks such as sentiment analysis, machine translation and text completion will be implemented in practical labs and assignments. Students should have good working knowledge in Python and some basics of deep learning (e.g. taken Deep Learning 1). | |||||||||
| EAS5214 | Gender and East Asian Women | 3 | 6 | Major | Master/Doctor | 1-4 | East Asian Studies | - | No |
| The goal of this course is to examine how gender or femininity was represented in modern East Asian culture and what social, historical, and intellectual contexts it contained. Moreover, this course will take into account women's self-representation and its shifting modes by examining some historical events. | |||||||||