Donghui Yan, PhD

Associate Professor

Mathematics

Research Website

508-999-8746

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Liberal Arts 394F

Education

University of California, BerkeleyPhD in Statistics

Teaching

  • Statistics
  • Machine learning
  • Data Science

Teaching

Programs

Teaching

Courses

Foundational topics in data science. Students will learn a broad range of data science skills applicable across different domains, including social sciences, finance, crime and justice, social networks, and engineering. Students will develop statistical and computational thinking skills, and they will apply these skills to real-world datasets. Specific topics include applied data problems, statistical software, data frames, descriptive statistics, natural language processing, data storage, data merging, linear regression, and data mining. The core skills developed in this course lay a foundation for more advanced coursework in data management, visualization, exploratory data analysis, and machine learning. No prior knowledge of programming or statistics is required.

Application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based capstone course provides students with a framework in which students gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended for data science majors only.

Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.

Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.

Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.

Continuation of MTH 332. Covering topics are advanced mathematical statistics topics, including detailed hypothesis testing, linear models, and regression analysis. This course also covers concepts and selected algorithms in machine learning.

Research

Research Interests

  • Statistics
  • Machine learning
  • Data mining
  • Data science
  • High dimensional statistical inference
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