Johns Hopkins, founded in 1876, is America's first research
university and home to nine world-class academic divisions working
together as one university.
Johns Hopkins University
Advanced Academic Programs
Data Analytics and Policy Program
Adjunct Faculty: Data Visualization and Machine Learning
The Advanced Academic Programs (AAP) is a division of the Krieger
School of Arts and Sciences at the Johns Hopkins University (JHU).
As the nation's oldest and one of the most prestigious research
universities, Johns Hopkins offers high-quality master's degrees
and post-baccalaureate education to students in the mid-Atlantic
region and online. In addition to the online programs, AAP also
offers master's degree programs at its Washington, DC Center and at
the Homewood campus in Baltimore, MD. AAP has approximately 18,000
enrollments each academic year. JHU is committed to hiring
candidates who, through their teaching and service, will contribute
to the diversity and excellence of the academic community.
The Advanced Academic Programs (AAP) division seeks one or more
non-tenure track adjunct faculty to develop and teach courses
within the Master of Science in Data Analytics and Policy program.
Specifically, we are seeking instructors qualified to teach Data
Visualization (470.673) and/or Machine Learning Methods and
Applications (470.667). Applicants may also be considered for other
teaching opportunities in the program. The course(s) will begin
their development processes in January 2024 and will be taught in
the fall 2024 semester and beyond. The courses will be taught fully
online (asynchronously) and/or onsite in Washington, DC. Candidates
with online course development and teaching experience and those
with experience teaching and engaging students from diverse
backgrounds are of particular interest.
Data Visualization is a core course in our curriculum, introducing
learners to exploratory data analysis and visualization in R,
Python, and Tableau. Interactive visualization and dashboard
development are also included in the course.
Machine Learning Methods and Applications is an introductory course
in machine learning, including topics such as linear modeling for
prediction, classification, tree-based methods, support vector
machines, and an introduction to deep learning and neural
Full course descriptions and an overview of our full curriculum are
available on the
Faculty duties include collaborating with the program director and
AAP’s instructional design team on course design in the spring and
summer of 2024. In fall 2024, the faculty member will serve as the
instructor for the course(s).
- An advanced degree with training in quantitative methods, with
a master’s degree at minimum (e.g., political science, sociology,
business analytics, economics, geography, public policy,
psychology, or similar)
- Experience with statistical programming, data visualization,
machine learning, and data analytics
- A terminal degree with training in quantitative statistical
methods (e.g., political science, sociology, business analytics,
economics, geography, public policy, psychology, or similar)
- 1-3 years of graduate or advanced undergraduate-level teaching
- Online teaching experience
- Experience or interest in multimedia course content
- 1-3 years of professional experience with applied statistics,
predictive modeling, machine learning, database management, and
related data science tools. An equivalent record of scholarly work
is also desirable.
The position will remain open until filled. For best consideration,
please apply by August 21, 2023.
Candidates must submit the following:
- Cover letter
- Curriculum vitae
- Teaching evaluations for two most recently taught courses, if
the applicant has teaching experience.
- Sample syllabi for relevant data analytics course(s), if
- transcript for your highest degree awarded
The selected candidate will be expected to undergo a background
check and submit references upon request.
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its faculty, staff, and students. To that end, the university does
not discriminate on the basis of sex, gender, marital status,
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individuals access to all academic and employment programs,
benefits and activities on the basis of demonstrated ability,
performance and merit without regard to personal factors that are
irrelevant to the program involved.
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employment, The Johns Hopkins Institutions require all employees
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