The Statistics Department had a productive retreat in January 2012 focused on our curriculum. However, this was only the start of rethinking. Here are further thoughts.
- Introductory Courses: Develop a more uniform and engaging presentation of introductory courses. Provide leadership for Lecturers of courses, now aimed at biological sciences (371), engineering (224 and 324) and a more diverse, general audience (301). Develop pedagogical tools to enable consistent instruction and to train both Lecturers and TAs. Renumber introductory courses to reflect level and provide obvious placement in course numbering.
- Statistical Languages: Provide campus leadership in training of statistical language concepts and tools. This should include best practices for introductory, intermediate and advanced audiences. While this has begun in Spring 2012 with introductory data analysis with R, it should expand to include other statistical languages (Stata, SAS, Julia) and should complement offerings in Computer Science (notably CS 368). The primary course delivery mechanism will be Stat 327, which uses 5-week modules for focused instruction. In addition, develop pedagogical materials for introductory courses aimed at an audience that is largely required to take one statistics course (say using Excel/Calc or Stata), but with opportunities for interested, creative students who want to go beyond.
- Undergraduate Program: Streamline and rationalize upper division/intermediate course offerings in 300-400 range to provide a coherent, partially ordered sequence leading to BS. Include opportunities for research experience and exposure to big data. Emphasize both methodological concepts and use of statistical language tools. An excellent beginning was accomplished this year, but it should be viewed as a first step toward a world-class undergraduate program.
- Graduate Program:
- Traditional MS: Refine and condense curriculum so that it is possible to get a MS in one year. This would include rethinking the instruction of linear models to have core masters-level material in one semester, coupled with a second semester on a new course on data analysis methodology and practice.
- Traditional PhD: Refine and condense curriculum so that graduates can complete a strong PhD in 4 years, complete with extensive research experience potentially leading to multiple high-impact publications. This will involve rethinking our core graduate courses (foundations 709-710 and linear models 849-850) in ways that best serve the PhD program. Ideally for linear models, there is one methods semester aimed at both MS and PhD, followed by a theory semester aimed at PhD only. Simplify elective options even further to offer key graduate training in a cafeteria-style of subjects, depending on student and faculty interest, with the aim of enhancing entry to dissertation research. This might involve combining multiple topics now in specialized courses into a few courses, such as “large sample inference”, “dependent data”, “hierarchical models”, “variable and model selection”. Faculty might teach 5-8 week modules within these elective sequences.
Educational innovation offers flexibility in modes of teaching. This will take time to develop. One important aspect of such flexibility is to enable faculty to have varied credit based on the type of teaching they undertake. Large, introductory courses should count as more than one course, while teaching an elective module (a statistical language, or an advanced graduate topic in 5-8 weeks) should count for something less than one course. In addition, advising effort should be given some form of formal instructional credit, and research experiences, including lab-style meetings, should also be recognized.