Karle Flanagan

Teaching

Current and Recently Taught Courses

Stat 100: Introduction to Statistics [Spring 2020], [Fall 2019], [Spring 2019], [Spring 2018]
- Statistics 100 is an introductory course designed for people who have never taken statistics before. It fulfills the QR 1 (quantitative reasoning) gen-ed requirement. Most of the students who take Stat 100 say they are not "math people", but still do great in the course! Stat 100 is unique because we do not teach statistics by memorizing formulas, instead we make sure students understand the concepts and ideas by using our own data taken in class through surveys, a fill in the blank notebook, pre-lectures videos, and a wide variety of tools to help facilitate learning for a large diverse group of students.
Stat 107: Data Science DISCOVERY [Spring 2020], [Fall 2019], [Spring 2019]
- Stat/CS/IS 107 is Illinois' first introductory data science gen-ed. This course shows students the intersection of statistics, computation, and real-world relevance. Students perform hands-on-analysis of real-world datasets using Python to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design. This course also fulfills the QR 1 (quantitative reasoning) gen-ed requirement and has no statistics or programming prerequisites. The next big thing at Illinois is Data Science and it starts with DISCOVERY!
Stat 593: Statistics Internship [Website]
- Stat 593 is the course that graduate students can take while completing a supervised, off-campus experience in a field in which statistics and data science plays an important role.
Stat 200: Statistical Analysis [Fall 2018]
- Survey of statistical concepts, data analysis, designed and observational studies and statistical models. Statistical computing using a statistical package such as R or a spreadsheet. Topics to be covered include data summary and visualization, study design, elementary probability, categorical data, comparative experiments, multiple linear regression, analysis of variance, statistical inferences and model diagnostics. May be taken as a first statistics course for quantitatively oriented students, or as a second course to follow a basic concepts course.