faculty
Donghui Yan, PhD
Associate Professor
Mathematics
Contact
508-999-8746
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Liberal Arts 394F
Education
University of California, Berkeley | PhD in Statistics |
Teaching
- Statistics
- Machine learning
- Data Science
Teaching
Programs
Programs
- Applied Statistics
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Mathematics BA, BS
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.
A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.
A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.
For PhD students who plan to take the PhD Comprehensive exam within the next 3 months. Up to 6 credits may be applied to either doctoral dissertation or MS thesis (should student not pass Comprehensive exam). Graded P/F.
For PhD students who plan to take the PhD Comprehensive exam within the next 3 months. Up to 6 credits may be applied to either doctoral dissertation or MS thesis (should student not pass Comprehensive exam). Graded P/F.
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 awards
- $ 457,478 awarded by Office of Naval Research for UMassD MUST IV: Knowledge Augmented Adaptive Learning of Evolving Models for Large Sensor Data Streams
Research
Research interests
- Statistics
- Machine learning
- Data mining
- Data science
- High dimensional statistical inference