Data Science Education and Career Paths

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  3. Data Science Education and Career Paths

Special Interest Group Co-Chairs: Tyler McCormick and Sarah Stone

Training in data science is needed at all levels in academic research, from the undergraduate student to the tenured faculty member. A major challenge to data science training is that it is inherently multidisciplinary, requiring training in computer science, statistics, applied mathematics, and one or more domain areas. Traditional university curricula do not provide this kind of broad data science training, and often data science courses are relegated to specific departments with no consideration of cross-disciplinary needs.

The Data Science Education and Careers Special Interest Group works to develop innovative teaching methods and formats to offer both formal and informal training in data science skills at undergraduate, graduate, and professional levels. Viewing education in its widest sense, the aim is to make data science technology and expertise far more accessible within universities and beyond. This group has created a framework for transcriptable Data Science Options that have now been rolled out in departments across the UW campus at the undergraduate and graduate level. For more information visit,

As we grow this new generation of researchers whose work depends crucially on the analysis of massive, noisy, and/or complex data and can require substantial curation and/or development efforts, we need to think about how to support these new career paths within and beyond the academy.  Data science career paths are currently complicated by strong competition from industry, a tendency by universities to measure productivity using only traditional metrics (e.g., journal publication) that do not reflect the scholar’s full contribution, and a general failure to provide a sufficiently supportive and meaningful environment and culture.

We see persistent, complex, and deep-rooted challenges for the career paths of people whose skills, activities, and work patterns do not fit neatly into the roles and success metrics of traditional academia. However, data science in research universities requires precisely the kind of complex, long-term interdisciplinary work with methodological and engineering efforts that leads to “low performance” under traditional metrics and “slow progress” and “lack of fit” in existing career tracks. The Data Science Education and Careers Special Interest Group seeks to develop and promote institutional policies that facilitate career development for data scientists at all levels, to build a community of data science practitioners across disciplinary fields, and to define career tracks and positions for data scientists in academia.

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