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.
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.
Students research a topic of their choice in scientific computing over two successive semesters. Research skills taught include literature and web searches, reading scientific papers, and organizing and keeping research records.
Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.
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.
Study under the supervision of a faculty member in an area covered in a regular course not currently being offered. Conditions and hours to be arranged.
Study under the supervision of a faculty member in an area covered in a regular course not currently being offered. Conditions and hours to be arranged.
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