Department of Artificial Intelligence and Data Science
Empowering a Smart Future, Cultivating Digital Talent
The Department of Artificial Intelligence and Data Science was established in August 2022, offering majors in Artificial Intelligence, Data Science and Big Data Technology, and a Higher Mathematics Teaching and Research Office. The Higher Mathematics Teaching and Research Office is primarily responsible for organizing and implementing the teaching tasks of higher mathematics, linear algebra, engineering mathematics, and other courses across the university.
Major Development
The department adheres to the core philosophy of “Technology Empowerment, Innovative Education” and is committed to cultivating application-oriented talents that meet the demands of the digital age. The department has significant disciplinary advantages and has constructed a curriculum system covering cutting-edge fields such as intelligent computing, data analysis, and machine learning. The professional courses are closely integrated with enterprise needs, incorporating real project cases to strengthen practical teaching. Students have achieved remarkable results in various academic competitions such as the “China College Computer Competition” and the “Big Data Challenge”, demonstrating solid professional competence.
Artificial Intelligence Major
This major adheres to the core philosophy of “Technology Empowerment, Innovative Education”, focusing on the talent needs of the digital age, and is dedicated to cultivating high-quality application-oriented talents with a solid professional foundation, innovative thinking, and practical abilities, creating skilled “dual certificate graduates” with both a bachelor’s degree and Huawei vocational certification. It employs a cutting-edge curriculum system:
1. Core Fundamentals: Machine Learning / Deep Learning / Natural Language Processing / Computer Vision / Knowledge Graph / Large Language Models / Intelligent Agents
2. Special Integration: Huawei Atlas / Huawei CANN / Huawei MindX / Huawei ModelArts / Huawei MindSpore
3. Practical Empowerment: Huawei Project Case Studies / Huawei Ecosystem Enterprise Internships / Huawei ICT Competitions
4. Three-Pronged Approach: Integrating Certification into Education, Integrating Practical Experience into Education, Integrating Job Skills into Education
Data Science and Big Data Technology Major
The Data Science and Big Data Technology major is market-oriented, based in Guangdong, and serves the Greater Bay Area. It adheres to the school’s educational direction, focusing on market demand and employment orientation, scientifically constructing a training system centered on application capabilities, cultivating high-quality application-oriented talents with comprehensive development in morality, intelligence, physical fitness, aesthetics, and labor, possessing good political quality and moral cultivation, as well as big data thinking and application technology. With a core focus on “Three-Dimensional Capability Advancement”, it builds a phased training system of “Foundation – Innovation – Application”, highlighting the characteristics of multidisciplinary integration. The curriculum system deeply integrates data technology, analytical techniques, and industry needs, offering integrated courses that combine job skills, competitions, and certifications, enhancing practical capabilities through enterprise projects, competitions, and vocational certifications; it also adds modular courses oriented towards business processes and work tasks, precisely aligning with job positions. The major’s advantages and characteristics include:
Technological Frontiers: Focusing on core technologies such as distributed computing and AI model optimization, establishing a university-level “Key Laboratory of Big Data and Intelligent Computing”; introducing enterprise-level teaching platforms from Teddy Company to support experiments with hundreds of millions of data.
Deep Integration of Industry and Education: Focusing on cutting-edge fields such as intelligent big data analysis, machine learning algorithm applications, and data visualization, creating a platform that integrates “teaching training + project development + achievement transformation”; introducing core technical resources from Huawei ICT Academy and Teddy Company (big data processing frameworks, AI development tools, industry datasets, etc.), allowing students to engage with real enterprise-level technical environments. Through stable school-enterprise cooperation channels, it provides students with rich, diverse, and high-quality internship and project practice opportunities that align with their majors.
Outstanding Student Innovation Ability: The Data Science and Big Data Technology major, guided by the philosophy of “Technology Rooted in Industry, Data Driven Future”, has formed a distinctive feature of “solid foundation, strong practice, and emphasis on innovation” through the “Three-Dimensional Capability Advancement” training model and the integrated curriculum system of “job skills, competitions, and certifications”. In the future, it will continue to deepen the integration of industry and education, exploring emerging fields such as AIGC and multimodal data analysis, to supply application-oriented data talents for the digital economy development of the Greater Bay Area.
Faculty Development
The department has a total of 44 faculty members, including 4 professors (including other senior titles) and 11 associate professors (including other associate senior titles); 42 hold master’s degrees or higher, including 5 with doctoral degrees; 35 are young and middle-aged teachers. In the past two years, the faculty team has led 4 provincial-level research projects, 3 university-level research projects, 14 university-level teaching reform projects, and 2 university-level first-class course constructions. They have published over 50 papers at the provincial level and above, including 6 high-quality SCI papers, authored 3 textbooks, and obtained 2 invention patents, providing solid support for the development of the discipline.
Department Leaders and Discipline Leaders
Yao Zheng’an
Vice President of Guangzhou Xinhua College, leader of the Artificial Intelligence major, PhD, Professor at the School of Mathematics, Sun Yat-sen University, doctoral supervisor, currently serving as the chairman of the Guangdong Mathematical Society, executive director of the Chinese Society for Industrial and Applied Mathematics, and director of the Mathematics Teaching Guidance Committee of Guangdong Province, as well as a member of the 7th and 8th Provincial Committees of the Guangdong Association for Science and Technology.
Xiong Jian
Department Head, Dean of the Artificial Intelligence Industry College, PhD, Professor, former head of the Statistics Department at Guangzhou University, and postdoctoral supervisor.
Hu Jianxun
Chief Professor, Professor at the School of Mathematics, Sun Yat-sen University, doctoral supervisor. Recipient of the National Outstanding Youth Fund, selected as a national candidate for the New Century “Hundred Thousand Talents Project” by the Ministry of Human Resources and Social Security.
Zhou Shiyong
Discipline Leader, PhD, Professor at Peking University. Executive director of the Chinese Seismological Society, member of the Solid Earth Physics Committee of the Chinese Geophysical Society, former director of the Geophysics Institute at Peking University (2012-2018), and chairman of the Seismology Professional Committee of the Chinese Seismological Society (2015-2020).
Wang Yiju
Discipline Leader, PhD, Professor, master’s supervisor.
Educational Achievements
The educational achievements are substantial, guiding students to participate in research projects and innovation and entrepreneurship practices through models such as “mentorship” and “project-driven” approaches, encouraging and guiding students to engage in practical projects and competitions related to artificial intelligence and big data. Through the accumulation of experience from actual projects and the competitive motivation of competitions, students’ practical and innovative abilities have been enhanced. In the past two years, students have participated in various academic competitions, winning over 50 awards at provincial and national levels. They have received first and second prizes in the National College Student Mathematical Modeling Competition and the National College Student Mathematics Competition, with over 30 individuals awarded cumulatively.
Some Award Certificates
Graduates Have a Wide Range of Employment Opportunities Across Various Industries
They can work in government agencies, enterprises, and institutions (especially in Huawei’s technology ecosystem) in roles related to autonomous unmanned intelligent control, big data and knowledge automation, machine vision, and multimedia intelligence, focusing on technical design, development, and engineering management in the field of artificial intelligence.
They can also work in business enterprises, government agencies, finance, healthcare, and research institutions (especially in Huawei’s technology ecosystem) in roles related to big data analysis, processing, services, development, and utilization, as well as big data system integration and management maintenance, while possessing information management and software development capabilities.
Special Courses
Machine Learning Course
The Machine Learning course is a core course of the Artificial Intelligence major. It mainly teaches the basic concepts of contemporary machine learning theory and typical applications of modern machine learning methods in the field of artificial intelligence. Through this course, students will understand the foundational knowledge related to artificial intelligence, master algorithms and methods for data classification and clustering based on the Python environment and libraries such as Scikit-learn, and comprehend the main principles of machine learning. The course aims to deepen students’ overall understanding of machine learning, familiarizing them with some classic and commonly used machine learning methods and cases, such as K-means, linear regression, support vector machines, and naive Bayes. As an introduction to deep learning, it covers the basics of convolutional neural networks and their structures.
Artificial Intelligence Innovation and Practice Course
The Artificial Intelligence Innovation and Practice course is a series of courses developed in collaboration with the FabLab (Xinhua Maker Lab) innovation center, combining the new teaching model of MIT (Massachusetts Institute of Technology) Fab Academy. The course builds a teaching website and offers interdisciplinary creative design challenges, cultivating students’ hands-on practice and innovation abilities. The course encourages students to master core skills in artificial intelligence and digital manufacturing through project-based learning, allowing them to independently complete the entire process of an artificial intelligence innovation project from “demand analysis – solution design – development implementation – testing optimization” based on their proficiency in using open-source hardware (Arduino) and AI development frameworks (PyTorch).
Probability Theory and Mathematical Statistics Course
The Probability Theory and Mathematical Statistics course is a core foundational course for the Mathematics Science and Big Data Technology major and is a university-level first-class course. The course aims to solidify students’ mathematical foundations and cultivate data analysis thinking through a teaching model that combines theoretical derivation with practical problems, helping students master core methods for uncertainty analysis, statistical inference, and decision-making, laying a solid foundation for solving complex problems in fields such as artificial intelligence, data science, and biomedicine.
The course teaching team consists of experienced professors and young key teachers, all with rich teaching experience and solid research backgrounds, leading multiple national and provincial-level research and teaching reform projects. After years of accumulation, the course team has constructed a “theory-case-experiment” integrated teaching resource system:
Teaching Model: Relying on the Super Star Learning Platform, a hybrid teaching loop is established through “pre-class preparation – in-class discussion – post-class expansion”, enhancing student participation through online discussions, real-time quizzes, and virtual simulation experiments;
Case Teaching: Designing interdisciplinary cases to guide students in abstracting probability models from practical problems, using statistical software (R/Python/SPSS) to complete data modeling and result interpretation;
Integration of Ideological and Political Education: Incorporating topics such as “data ethics” and “statistical misuse” to cultivate students’ rigorous scientific attitudes and social responsibility.
Dr. Chen Minqiong Teaching Image
Big Data Analysis and Mining Course
The Big Data Analysis and Mining course is a core feature course of the Data Science and Big Data Technology major and is a key construction course under the “New Engineering” background. The course is guided by “technology-driven, industry-empowered” principles, focusing on cultivating the full chain capabilities of big data processing, covering data collection, cleaning, storage, analysis, and visualization, and integrating machine learning and deep learning technologies to cultivate students’ abilities to solve practical problems in e-commerce, finance, healthcare, and other fields, providing high-quality application-oriented talent support for digital transformation.
Course Teaching Model
The course teaching team consists of interdisciplinary teachers, covering computer science, statistics, and industry application experts, with team members leading 2 provincial-level teaching reform projects and 2 university-level teaching reform projects.
Course Construction Features:
“Dual Engine” Teaching Model + Integration of Ideological and Political Education: Technical Engine: Building experimental environments based on platforms such as Alibaba Cloud and AWS, strengthening practical skills with tools like Hive, Flink, and TensorFlow; Industry Engine: Collaborating with companies like Huawei, Xunfang, and Teddy to develop real cases, achieving “learning by doing”.
Integration of Ideological and Political Education: Through discussions on topics such as “data privacy protection” and “algorithm fairness”, guiding students to establish a sense of technological ethics.
The course follows the reform mainline of “solid foundation, strong practice, and emphasis on innovation”, constructing a three-tier capability cultivation system: foundational level – mastering core algorithm principles such as data preprocessing, association rule mining, and classification/regression models; application level – proficiently using big data toolchains to complete the full process from data to insights; innovation level – cultivating the ability to abstract cross-domain problems and design solutions through school-enterprise cooperation projects, shaping “dual-skilled talents” in technology and business.
Huawei Big Data Vocational Certification Series Courses
These are core courses for the Data Science and Big Data Technology (Excellence Class). They primarily integrate Huawei’s big data vocational certification courses with relevant professional courses in big data technology, based on Huawei’s technical products, solutions, and practical case knowledge/tools, enabling students to effectively participate in Huawei’s vocational certification exams after completing relevant professional courses, significantly enhancing their competitiveness in the job market. Typical courses include:
HCIA Series Courses: Including HCIA-Big Data (Big Data Engineer), covering professional courses such as Introduction to Data Science, Big Data Platform Technology, Big Data Stream Processing, etc.
HCIP Series Courses: Including HCIP-Big Data (Big Data Development Engineer), covering professional courses such as Python Data Analysis, Data Collection and Processing, Big Data Platform Technology, Big Data Stream Processing, Data Mining, Data Visualization, etc.
Special Activities
2024 Forum on High-Quality Development of the Low-Altitude Economy Empowered by Digital Intelligence
From December 25 to 26, 2024, the “2024 Forum on High-Quality Development of the Low-Altitude Economy Empowered by Digital Intelligence” will be held at the Dongguan campus, co-hosted by the school and the Guangdong-Hong Kong-Macao National Applied Mathematics Center. The forum will gather elites from the education, academic, and industry sectors to discuss the development trends, technological innovations, and industrial applications of the low-altitude economy under the theme “Digital Intelligence Leads, Wings Move the Future”, providing valuable insights and solutions for promoting the high-quality development of the low-altitude economy; it advocates and establishes the “Guangdong-Hong Kong-Macao Digital Intelligence Low-Altitude Economy Industry-Education Integration Development Alliance”, aiming to build a bridge for industry-education integration.
Annual Meeting of the Guangdong Provincial Higher Education Mathematics Teaching Guidance Committee and Expanded Meeting on the Ideological Work of Mathematics Textbooks
On January 16, 2025, Guangzhou Xinhua College will hold the annual meeting of the Guangdong Provincial Higher Education Mathematics Teaching Guidance Committee and the expanded meeting on the ideological work of mathematics textbooks. The meeting is co-hosted by the Guangdong Mathematical Society, Guangzhou Xinhua College, and Higher Education Press, with the Dongguan Branch of the Guangdong-Hong Kong-Macao National Applied Mathematics Center, the School of Mathematics at Sun Yat-sen University, the Department of Artificial Intelligence and Data Science at Guangzhou Xinhua College, and the Research Office of Guangzhou Xinhua College jointly undertaking the event, with the Guangdong Teaching Service Center of Higher Education Press as a co-organizer. The meeting will gather experts and scholars from various universities in Guangdong Province to discuss the innovative development path of mathematics education in the new era.
Academic Sharing Session
On the morning of December 27, 2024, the Department of Artificial Intelligence and Data Science at Guangzhou Xinhua College will hold an academic sharing session themed “Reflections and Explorations on AI for Math and Math for AI – Discussing the Teaching and Learning of Mathematics in the Era of Artificial Intelligence”, with Professor Hao Zhifeng, a well-known scholar in the country, doctoral supervisor, and Vice President of Shantou University, as the speaker. The event will focus on the intersection of artificial intelligence and mathematics, as well as the profound impact of the era of artificial intelligence on mathematics education. This sharing session will not only provide a valuable academic exchange platform for the integrated development of artificial intelligence and mathematics but also offer new ideas for the reform of mathematics teaching in the era of artificial intelligence, broadening the academic horizons of faculty and students in the department, and laying a solid foundation for the mutual empowerment of mathematics and artificial intelligence.
International Cooperation
Negotiating cooperation projects with Singapore University of Technology and Design, and signing a framework agreement for cooperative education with UUNZ Business School and Tasman College in New Zealand. This provides students with an international learning platform. By introducing internationally advanced professional courses and practical teaching, students can enhance their professional skills in practice, which not only aids their academic and career development but also promotes personal growth and the cultivation of global awareness.
Negotiating cooperation projects with Singapore University of Technology and Design
Students in the department participate in external exchange activities, which can comprehensively enhance their qualities and abilities, laying a solid foundation for their future learning, work, and personal development. Through external exchange practice activities, students can be exposed to the cultures, values, and lifestyles of different countries and ethnicities, broadening their international perspectives, breaking down preconceived notions and biases about the world, cultivating global awareness and cross-cultural understanding, learning to communicate and interact with others in different cultural contexts, understanding cultural differences, and improving communication efficiency and interpersonal skills.
Students participating in social practice activities organized by the Royal University of Udon Thani in Thailand
The Department of Artificial Intelligence and Data Science has a distinct cultural characteristic, themed around “Digital Intelligence Integration”, creating a strong atmosphere of technology and innovation. The department regularly holds the “Frontier Forum on Artificial Intelligence and Data Science”, inviting industry experts and well-known figures from the academic field to campus for seminars and lectures, sharing the latest technological developments, industry trends, and application cases, allowing students to understand the most cutting-edge information in the industry; it also conducts a “Digital Intelligence Cultural Festival”, hosting data governance and data analysis competitions to stimulate students’ enthusiasm for learning. At the same time, it has established a “Digital Intelligence Innovation Laboratory” to provide an open research and practice platform for students, helping them stand at the forefront of the technological wave.
Looking to the future, the Department of Artificial Intelligence and Data Science will continue to uphold the philosophy of “Technology Empowerment, Innovative Education”, continuously optimize major construction, enhance talent cultivation quality, and strive to cultivate more outstanding talents for the school’s high-quality development and the digital age.
Source | Department of Artificial Intelligence and Data Science
Compiled by | Long Haofen
Reviewed by | Xiong Jian
Edited by | Feng Zhanhao
Chief Editor | Feng Zhanhao
Approved for Release | Tao Qiang
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