香港科技大学（广州）2023本科专业介绍
数据科学与大数据技术专业（理学士）
大数据作为国家基础性战略资源,已经成为当前学术界和工业界的关注焦点。数据科学与大数据技术是一门以计算机科学、统计学、数学为支撑学科，涉及人工智能等领域的交叉融合学科。本专业旨在培养新型数据科学复合型人才。学生将会兼备扎实的学科理论基础、良好的科学素养和社会责任感，能够以系统的数据思维解决实际应用中复杂问题，并应对现实世界中数据科学与大数据的挑战。随着大数据相关产业的迅速发展，数据科学和大数据人才已成为市场上迫切需要的高端型人才，本专业学生将迎来更为广泛的就业空间。
本专业毕业生未来能在数据科学与大数据科领域深造，从事科研和教学工作，也可以成为社会不同行业数字化转型和数字化创新的中坚力量。
在港科大（广州），所有专业的本科生都应修习完成30学分的通识课程，并需在本科一年级、二年级修完大部分通识课程。
本科通识课程分为三大课程层级：第一层级是基础课程，第二层级是拓宽课程，第三层级是体验课程，三个层级课程对学生的学术和能力提升有不同要求。通过修习通识课程，学生不仅可以更加胜任专业学习，还可以收获艺术、人文和文化素养的提升。
港科大（广州）为本科生在其一年级与二年级提供不同层级的基础课程，涵盖数学、物理、化学、生物、计算机科学、工程、经济和社会学等学科领域知识。
以下为数据科学与大数据技术专业的部分专业基础课程：
计算机科学：
UFUG 1601 Introduction to Computer ScienceUFUG 2601 C++ Programming微积分：4选2 (UFUG 1102 或 UFUG 1105) 和 (UFUG 1103 或 UFUG 1106) UFUG 1105 Honors Calculus IUFUG 1106 Honors Calculus II
UFUG 2104 Applied Statistics
UFUG 1301 General Chemistry
UFUG 1302 Honors Chemistry I
UFUG 1401 General Biology I
UFUG 1501 General Physics I
UFUG 1502 General Physics II
UFUG 1503 Honors General Physics I
UFUG 1504 Honors General Physics II
UFUG 1701 Introduction to Civil EngineeringUFUG 1801 Principles of Economics
UFUG 1811 Quantitative Data Analysis for Social Research
本专业理论教学与实践课同时开展，通过多种不同的实践性教学活动，如实验上机和项目设计，学生能充分发挥学习效能，并运用所学知识和技能来解决科学、技术和社会中的实际问题。
入门指南类：2选1
Introduction to Data Science and AnalyticsIntroduction to Artificial Intelligence 基础知识类：DSAA 2043 和 (DSAA 2085 或 DSAA 2088) Design and Analysis of AlgorithmsMathematics for Data Science数据科学类：(DSAA 2011 或 AIAA 3111) 、 DSAA 2012 和 DSAA 2031Introduction to Data MiningDatabase Management Systems English Communication I for Information Hub ProgramsEnglish Communication II for Information Hub Programs
Final Year Capstone Project
数据科学与大数据技术作为一门交叉融合学科专业，为学生提供丰富的选修课程和个性化学习路径，帮助学生探索自己的兴趣所在，打开梦想的视野。
本专业学生除了从选修科目清单中选择8门课程，还可选修其他学域的任2门课程。
以下为数据科学与大数据技术专业的部分专业选修课程：
DSAA 1085 概率与统计
Probability and Statistics
Advanced Theories in Computing
DSAA 2042 计算机体系结构和系统
Computer Architecture and Systems
DSAA 2049 高级编程语言
Advanced Programming Languages
DSAA 3041 高级算法
Advanced Algorithms
Introduction to Natural Language Processing and KnowledgeDSAA 3052 计算机视觉与多媒体相关的数据科学Data Science for Computer Vision and MultimediaIntroduction for Reinforcement LearningBayesian Models and ApplicationsIntroduction to OptimizationAdvanced Machine Learning and Deep LearningCloud Computing and Big Data SystemsIntroduction to High-Performance and Parallel ComputingData Management for Data ScienceDeep Learning for ScienceData Science for Cross-disciplinary ApplicationsSpecial Topics in Data ScienceData Science for Battery Technologies
其他数据科学方向：
Data Privacy and Security
DSAA 2011 机器学习
Machine Learning
专业必修课程。Machine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g., linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g., clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems.
DSAA 2031 数据库管理系统
Database Management Systems
专业必修课程。Topics include: principles of database systems; conceptual modelling and data models; logical and physical database design; query languages and query processing; database services including concurrency, crash recovery, security and integrity. This course will provide hands-on DBMS experience.
DSAA 4591 毕业设计项目
Final Year Capstone Project
专业必修课程。This course is an independent study or project under the directed guidance of a faculty member on a data science topic. A written report, presentation, and/or an examination are required. The course will also provide opportunities for students to practice their English skills (reading, writing, understanding, and presentation) via project-related activities. Credit load will be spread over the year.
