Julie Deeke

I am a Teaching Assistant Professor in the Department of Statistics at the University of Illinois, Urbana-Champaign.

As an educator, I provide my students with authentic experiences working with and analyzing data, building skills and gaining experience with tools in the process that can be applied to new problems.

I specialize in teaching large, applied courses, including Data Science Exploration (Stat 207, a second course in data science) and Methods of Applied Statistics (Stat 420/ASRM 450, an upper-level applied regression course). My scholarly interests include statistics and data science education, graduate student teaching development, instructional technology, and pedagogy for large courses.

jdeeke [at] illinois [dot] edu

703 S. Wright St, Room 44, Champaign, IL 61820

she/her/hers

CV

teaching:

Data Science Exploration (Stat 207): Summarizing, Generalizing, and Predicting is a 4 credit hour course. For students, this course includes 150-160 minutes of lecture and 80 minutes of lab each week. Tori Ellison and I have designed and published a free website with our course content. Check it out! I am regularly innovating and refining the course, including adding new activities to lecture and incorporating interesting data sets to labs, homework assignments, and projects.

Methods of Applied Statistics (Stat 420/ASRM 450) is an upper-level course, primarily attracting statistics minors and graduate students from other disciplines. We focus on fitting and applying linear models to data, along with interpreting the results and the implications for the underlying context. We use my interactive lecture notes along with Applied Statistics with R, an e-book by David Dalpiaz for the course.

presentations:

Most of my recent scholarly activity has resulted in workshops, breakout sessions, and presentations related to the development of data science education, graduate teacher development, and the effects of student choice on course performance and student perception. Below, you can find material related to recent presentations.

1. data science education
   • Building Cohesive Values and Learning Goals for a Sequence of Core Data Science Courses
   • Helping Students Select an Appropriate Analysis Technique in a Second Course in Data Science
   • Framing and Broadening Understanding of Research Norms in a Second Course in Data Science
2. graduate teacher development
   • Supporting professional development through teaching assistantships
   • Creating a Culture for Growth in TA Training
3. student choice
   • Promoting Student Choice in Initial Interactions with Pre-Recorded Lecture Material

graduate student teaching development:

I work with a large number of graduate teaching assistants and undergraduate course assistants for both of my large enrollment courses. Helping these graduate and undergraduate students develop their content knowledge, pedagogical skills, and leadership experiences is a rewarding component of my job.

Together with my colleague Kelly Findley, I have developed and facilitated a dedicated training for our first-year PhD students and our graduate teaching assistants in lab or discussion roles focused on building cohort unity, professional skills, and pedagogical knowledge in relation to fundamental statistics and data science topics. We have also been working together with others on a TA Training Committee to support our Statistics PhD students throughout their time in the program and on a Graduate Student Awards Committee to award those Statistics PhD students who have shown initiative and excellence in teaching and leadership.