The curriculum taught in introductory statistics courses rarely introduce students to the types of methodologies that have made statisticians and data scientists ‘sexy’. “Before they can learn advanced methods—such as randomization tests, bootstrapping, or CART (Classification and Regression Trees)—students need to have a foundation in basic methods” is a common refrain from introductory statistics instructors. In this talk, I will offer rationale for why advanced methods not only can, but should be included in an introductory course, and how they emphasize the type of statistical thinking that is often viewed as an important outcome for introductory students.  I will also share how we have done this in an introductory statistics course for social science students at the University of Minnesota. I will also offer suggestions and lessons learned from the classroom.