  Data science education provides tremendous opportunities but remains
inaccessible to many communities. Increasing the accessibility of data science
to these communities not only benefits the individuals entering data science,
but also increases the field's innovation and potential impact as a whole.
Education is the most scalable solution to meet these needs, but many data
science educators lack formal training in education. Our group has led
education efforts for a variety of audiences: from professional scientists to
high school students to lay audiences. These experiences have helped form our
teaching philosophy which we have summarized into three main ideals: 1)
motivation, 2) inclusivity, and 3) realism. To put these ideals better into
practice, we also aim to iteratively update our teaching approaches and
curriculum as we find ways to better reach these ideals. In this manuscript we
discuss these ideals as well practical ideas for how to implement these
philosophies in the classroom.
