Jevin D. West
Jevin West

Current Courses

Calling Bullshit: Data Reasoning in a Digital World (Fall 2020)
Calling Bullshit: Data Reasoning in a Digital World (Fall 2019)
INFO 371
Advanced Methods in Data Science (Spring 2019)
Calling Bullshit: Data Reasoning in a Digital World (Fall 2018)
INFO 180
Introduction to Data Science (Fall 2018)
INFO 371
Core Methods in Data Science (Spring 2018)
INFX 574
Data Science II: Machine Learning & Econometrics (Winter 2018)
Calling Bullshit in the Age of Big Data (Fall 2017)
Prior Courses

Calling Bullshit in the Age of Big Data (Spring 2017)
INFX 574
Data Science II: Machine Learning & Econometrics (Winter 2017)
INFO 370
Introduction to Data Science (Fall 2016)
INFX 575
Data Scaling, Applications & Ethics (Spring 2016)
INFO 370
Introduction to Data Science (Fall 2015)
Intro. to Data Science & Management, Sungkyunkwan Univ. (Summer 2015)
INFO 370
Introduction to Data Science (Spring 2015)
INFX 575
Data Scaling, Applications & Ethics (Spring 2015)
INFO 571
Data Science Seminar (Fall, Spring, Winter 2015)
INSC 570
Research Methods (Fall 2014)
INFO 498
Introduction to Data Science (Spring 2014)
INFX 598
Advanced Methods in Data Science (Spring 2014)
INSC 570
Research Methods (Fall 2013)
Data Science

I teach, mentor and design curricula in Data Science at the University of Washington. I am currently teaching INFO 370/371 and INFX 574/575. I co-developed, with Josh Blumenstock and Emma Spiro, the Data Science series for the graduate and undergraduate programs in the iSchool. This includes our core sequences for the MSIM and MLIS programs (INFX 572/573/574/575) and Informatics programs (INFO 370/371). We are currently designing new electives to build upon this core sequence.

I am actively involved in the Education Group at the eScience Institute. In collaboration with Magda Balazinska and department chairs across campus, we have developed a transcriptable option in Data Science. The idea is to (1) make data science courses available to any major and student on campus, (2) recognize students that have specialized in data science, and (3) leverage the strengths of our various departments at UW. Departments can design their own sequence of courses, depending on their needs and domain questions, but can leverage other courses and opportunities across campus. Currently, we have university approval or pending approval for the data science option in the following schools and departments: the iSchool, ACMS, Computer Science & Engineering, HCDE and Statistics. We are looking to add additional departments in subsequent years.

I am also on the steering committee for the new Masters Program in Data Science at UW. This is another multi-departmental data science program at UW. The committee has been in charge of developing the program, admissions and ongoing advisement for the program. We recently hired our new director of the program, Deborah Alterman, and enrolled our first class in the Fall of 2016. In addition, I have written a chapter with Jason Portenoy on the 'gold rush' in data science education across the country.

Calling Bullshit in the Age of New Data

Calling Bullshit Course

Carl Bergstrom and I have designed a new course that focuses on data reasoning. It is in response to the increased BS ('bad science') that we are seeing in academic discourse, especially in this new age of big data. The course will be initially aimed at first-year undergraduates, but our goal is to make the contents of the course freely available to anyone and everyone, inside and outside the university. We enourage you to visit our course website for more details.

Curriculum Development

  • Undergraduate Data Science Option, eScience Education Working Group (2014 - present)
  • Masters in Data Science, Profession & Continuing Education (2015 - present)
  • Data Science Track, MSIM Program, Information School (2013 - present)
  • Huckabay Fellowship, Center for Instructional Development and Research (2006 - 2007)
  • Summer Teaching Institute, Seattle School District (Summer 2008)
  • Howard Hughes RA for developing Experimental Evolutionary Ecology Lab (Summer 2006)
Teaching Awards


I advise post-docs, PhD students, Masters students and undergraduates working on projects in Data Science. Below are current and former students that have or are working on projects in big scholarly data, community detection, network analytics, and related areas. If you are interested in a project related to the Science of Science or Data Science, please feel free to contact me.

PhD Students (primary advisor):

PhD Students (co-advisor, committee):

PhD Students (GSR):

Masters Students:

Undergraduate Students:

Teaching Philosophy

The internet is dramatically changing the world of Education. Students can easily access unlimited education resources—from online courses to freely available tutorials, books and lectures. In my field, especially, students can learn about data science, programming, and statistics without ever stepping foot in a formal classroom. So what does a formal education at a formal university provide nowadays? This is a question that drives my teaching philosophy.

I want to be as human a teacher as possible. In class, I can read students confusion, excitement or reticence. These cues can be used to engage the students in discourse, no matter how big the class. Whenever possible, I try to flip the classroom—provide students with the content and questions before class and then solve the problems together in class. I include examples that are current and relevant to the given classroom. I initiate group discussions and project-based learning and give immediate, real-time feedback.

I stive to build in-person classes that are worth attending by leveraging the human interaction element that online education has not replaced (at least yet). Online education is a great resource for students. I don't want to replace it; I complement it.

Student Teacher

I reside on a campus because of teaching. It allows me to be the eternal student. Teaching is the ultimate show and tell, but it is not just from teacher to student. There exists a reciprocity of mentorship that only the unencumbered minds can offer. Who I am and where I am today is a reflection of my teachers, mentors and now my students. In some of my favorite classes, it was difficult to tell who was having more fun—the teacher or the students. I will continue to teach as long as this is true for me.

Field Trip With Students
Guest Lectures

Faculty Teaching Seminar
SKKU Visiting Program (Jan. 18, 2018)
DATA 556
Masters in Data Science (Dec. 6, 2017)
Data Science and Society Seminar (Jan. 26, 2017)
IMT 500
MSIM Foundations (Oct. 21 2016; Oct. 17, 2016)
INFO 371
Core Methods in Data Science (May 9, 2016)
Independent Study

Masters Students:

Undergraduate Students

Teaching Assistantships (TA)

BIOL 354
Foundations in Evolution and Systematics (Spring 2009)
BIOL 354
Foundations in Evolution and Systematics (Spring 2009)
Tutoring and Outreach


I spent several years teaching tennis. I was the Assistant Coach for Utah State University Men's and Women's Tennis team from 2000 - 2002. During that same time, I instructed players of all ages and abilites in community programs or local tennis clubs. Although not the typical classroom with books and papers, the tennis court provided a different kind of medium in which to develop my teaching and mentoring skills. I have found these skills to be paticularly useful in the actual classroom—most notably, in tailoring drills to a student's specific needs. Students have different strengths, different backgrounds and different perspectives. The challenge at a big university is scaling classes while at the same time preserving customized learning.

Scholarship of Teaching

Faculty Fellows, UW Center for Teaching and Learning (Sept. 9-12, 2013)

Scholarship of Teaching and Learning Symposium. University of Washington, Seattle, WA
The Missing Link PDF(April 2007)
J.D. West, K. Hall