Page 49 - Special Topic Session (STS) - Volume 3

STS515 Alison L. G. et al.
must engage, learn from, teach, and work with [researchers with expertise in
data management and computation].” Embracing computational thinking and
applications, while upholding our traditional strengths, is an important
prerequisite in this effort. In their epilogue, Efron & Hastie (2016) give a
historical summary of the shifting focus in Statistics between applications,
mathematics, and currently computation, all the while maintaining a principled
approach. Although excessive preoccupation with mathematical
underpinnings has led our discipline to periods of introversion, Efron & Hastie
also identify important recent developments fuelled by applications and
computation, the type of developments upon which the sustained success of
Statistics will ultimately be judged.
Parallel to asserting Statistics’ place in this new world of Data Science, there
is another effort to attract and educate the future generations of statisticians
who will populate it. The current hype around the Data Scientist profession,
with Glassdoor ranking it “Best Job in America” for the 4th year in a row
(https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm), is
fuelling unprecedented growth in Statistics programs’ enrolments (Pierson,
2017). But the rise of Data Science has also placed new demands on
undergraduate statistics education and provided new impetus for its reform.
Horton & Hardin (2015) recognize that “the traditional statistics curriculum
with mathematical foundations has not kept up with pressing demands for
students who can make sense of data” and call for curricula that “prepare
students to engage in the entire data analysis process.” As envisioned in the
most recent ASA guidelines (2014), this curriculum should provide a balance
between mathematics, computing, and applications, while offering many
opportunities for practice and skill-building. But even with such bold reforms,
the rapidly changing landscape of Data Science suggests our graduates will
face conditions they have not seen before. We claim that alongside teaching
new content and skills, our programs should also cultivate a new mindset, one
that will prepare students to learn on their own and apply their knowledge
flexibly. We propose a set of three traits and attitudes, namely inquisitiveness,
extroversion, and statistical thinking, which comprise this “adaptive statistical
mindset.”

2. Teaching Statistics in View of Data Science
Accompanying the enrolment growth in Statistics programs and the
proliferation of programs of study in Data Science (see, for example, NASEM,
2018), new and modified guidelines for both types of programs have been
created. These guidelines reflect the evolving understanding of the knowledge
and skills needed to learn from data in our current context. While statistical
thinking remains a core feature, the guidelines include calls for enhancing
computational skills to deal with larger and more complex data, greater

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