Double course. Open to senior majors in Sexuality, Women’s and Gender Studies who have received departmental approval.
Fall semester. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: an emphasis on written work, readings, and independent research. They will be required to meet regularly with the professor.
Open to senior majors in Sexuality, Women’s and Gender Studies who have received departmental approval.
Fall semester. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: an emphasis on written work, readings, and independent research. They will be required to meet regularly with the professor.
Independent reading course.
Fall and spring semesters. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: an emphasis on written work, readings, and independent research. They will be required to meet regularly with the professor.
Fall semester. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, writing intensive, use of computational software, independent research
Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies.
Fall and spring semesters. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, projects, use of computational software
Real world datasets are plagued by missing observations. Statistical software packages often ignore these cases by default, but there are better ways to approach the problem. This course will introduce students to the different missing data mechanisms and explore naive and modern methods for handling missing data. It will prepare students to read the current literature in this area and have broad appreciation for the implications of missing data.
Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.
Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.
This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment.
