覆盖本科到博士所有阶段！数据科学与分析学域2024秋季课程抢先看！

数据科学与大数据技术专业

This course introduces students to the world of computer science, data analysis, and artificial intelligence. Through a series of lectures and hands-on exercises, students will learn the basics of each of these disciplines, and how they can be used to solve real-world problems. It will cover the following topics: an introduction to computer science, including an overview of its principles and concepts; the basics of data analysis, including methods for collecting, organizing, and analyzing data; an introduction to artificial intelligence, including an exploration of its various applications and capabilities; and an examination of how computer science, data analysis, and artificial intelligence can be used in combination to solve real-world problems. Upon completion of this course, students will have a strong foundation on which to build more advanced knowledge in these exciting fields.

Prof Guoming TANG

Assistant Professor in DSA Thrust

This course covers programming and data structures using C++. In addition to basic programming concepts such as variables, arrays, pointers, and functions, students will learn about the standard data structures in C++, such as vectors, sets, maps, and queues. This course will also introduce the basics of object-oriented programming and some useful algorithms in the standard C++ libraries. Weekly laboratory experiments will provide hands-on experiences in programming.

Prof Weikai YANG

Assistant Professor in DSA & CMA Thrust

其他本科专业

This course introduces fundamental concepts, principles, and techniques of Java - a contemporary object-oriented programming language. Students will learn how to construct appropriate algorithms for the solution of certain problems, using variables, arrays, control statements, loops, recursion and data abstraction. Students will gain experience and confidence in the use of a high level programming language to implement algorithms.

Prof Zeyi WEN

Assistant Professor in DSA Thrust

Prof Wei ZENG

Assistant Professor in DSA & CMA Thrust

数据科学与大数据技术专业

Data science changes the way people process data in different areas. It has promoted the development of many subjects. This course introduces beginners to the whole lifecycle of data science problems and solutions. The course will help students comprehensively understand the basic knowledge of data science and use computer techniques to handle the real-life data science problems. Topics covered include: data collection and processing, computer-oriented modelling techniques, relationship with other mathematics, and case studies.

Prof Lei LI

Assistant Professor in DSA & INTR Thrust

Prof Jia LI

Assistant Professor in DSA Thrust

Prof Wei WANG

Professor in DSA Thrust

This course introduces core data structure and algorithms, which are fundamental to data science and analytics. As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs.

Prof Jing TANG

Assistant Professor in DSA Thrust

数据科学与分析硕博专业

大数据智能硕士专业

With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI. 

Prof Jia LI

Assistant Professor in DSA Thrust

Prof Wei WANG

Professor in DSA Thrust

A recent trend in Data Science and Machine Learning communities is to further boost the accessibility of Machine Learning techniques by reducing the tedious effort on learning model selection, hyper-parameter tuning, etc. Automatic Machine Learning (AutoML) aims to reduce this tedious effort and make Machine Learning easier to use. In this course, students will master the basics of AutoML, and understand key techniques including hyper-parameter optimization, feature engineering and meta-learning. This course also introduces common AutoML systems and covers real-world case studies on the applications of AutoML.

Prof Zeyi WEN

Assistant Professor in DSA Thrust

This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.

Prof Jing TANG

Assistant Professor in DSA Thrust

This course will teach students data science computing techniques. Topics cover: (1) Basic concepts of Data Science Computing and Cloud; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop; and (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark.

Prof Yuyu LUO

Assistant Professor in DSA Thrust

This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.

Prof Wei ZENG

Assistant Professor in DSA & CMA Thrust

In this course, we will introduce spatial and multimedia database management concepts, theories and technologies, from data representation, indexing, fundamental operations to advanced query processing. Challenges and solution for high dimensional data will also be introduced.

Prof Lei LI

Assistant Professor in DSA & INTR Thrust

This graduate-level course is designed for multi-discipline students to learn and apply industrial analytics techniques for problem-solving and process improvement in various industries. The course will emphasize the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology, along with other data-driven techniques for process improvement and problem-solving. Students will gain hands-on experience through case studies, assignments, and a term project. Prerequisites: Basic knowledge of statistics, data analysis, and engineering principles.

Prof Fu-Gee TSUNG

Chair Professor in DSA & CMA Thrust

Genetics and genomics are closely related fields, both analyzing massive biological data. Genetics studies specific genes and their roles in heredity, while genomics encompasses the entire genome of an organism to understand its complex systems. This course aims to introduce traditional computational biology and revolutionary advancements in machine learning that are transforming the fields of genetics and genomics. It begins with a very brief overview of conventional algorithms in genetics and genomics, setting the stage for a deeper exploration of how genetics and genomics have evolved with the advent of machine learning techniques. As the course progresses, students will be introduced to the latest machine learning methods, with an emphasis on deep learning, which has dramatically changed the landscape of genomics research. Through a combination of theoretical instruction and hands-on teamwork projects, this course is designed to equip students with the necessary skills to navigate and contribute to the field of genomics, leveraging machine learning to uncover new insights into genetic data and its implications for human health and disease.

Prof Yanlin ZHANG

Assistant Professor in DSA Thrust

Nowadays, the integration of machine learning (ML) into various applications has brought unprecedented opportunities as well as challenges. Machine Learning Security and Privacy is a comprehensive course designed to address the critical need for understanding and mitigating the security and privacy risks inherent in ML systems. The course begins by examining foundational concepts in machine learning and its applications across different domains. It then delves into the specific vulnerabilities and threats that arise in ML systems, such as adversarial attacks, data poisoning, model inversion, and membership inference. Furthermore, the course emphasizes the ethical considerations surrounding ML security and privacy, including the impact of biased datasets, algorithmic fairness, and responsible AI practices. By the end of the course, students will have acquired a deep understanding of the security and privacy challenges in machine learning, along with practical skills to design, implement, and evaluate secure ML systems. This course equips students with essential knowledge to navigate the complex landscape of ML security and privacy effectively.

Prof Xinlei HE

Assistant Professor in DSA & IoT Thrust