Page 50 - Special Topic Session (STS) - Volume 3

STS515 Alison L. G. et al.
attention to algorithmic approaches, and increased emphasis on skills needed
for professional practice, including communication and collaboration. Some
key features of the guidelines are as follows:
Revisions to the Guidelines for Assessment and Instruction in Statistics
Education (GAISE College Report ASA Revision Committee, 2016) for the first
course in statistics have increased emphasis on reasoning with multivariate
data and on engagement in a complete investigative problem-solving process.
In 2014, the ASA endorsed new guidelines for the curriculum of undergraduate
programs in statistics (ASA Undergraduate Guidelines Workgroup, 2014),
aiming to ensure continued relevance of graduates from such programs. In
comparison with previous ASA curriculum guidelines, the 2014 guidelines
have increased emphasis on analyzing complex data, modelling for prediction,
and computing, including the skills required to access and process complex
and large datasets. They also underscore the importance of developing the
skills needed to engage in statistical problem-solving applied to questions
from other domains.
More recently, guidelines for undergraduate programs in Data Science (De
Veaux et al., 2017 and NASEM, 2018) emphasize the integration of skills from
statistics, computer science, and mathematics, focused on the aspects of these
fields that are important for learning from data.
All three guidelines promote the importance of involvement in the
complete statistical process, including formulating questions, acquiring
suitable data, analyses, communication of results, and critical assessment of
each step that may lead to iterations of the process. Many of the emphases in
the guidelines are not new. For example, there has been a long-standing
conversation about the need for our students to develop a broad set of non-
technical skills to enable them to become effective contributors to the
solutions of problems in a variety of applications. Utts (2015) described four
themes that regularly appear in initiatives related to statistics education in the
ASA over its 175-year history. Among these is the need for statisticians to
develop “soft skills,” including the ability to communicate results to non-
technical audiences and function effectively as part of a team. Building on
these calls for reform and considering the rapid changes in the field, in the
next section we offer another perspective on teaching Statistics for an evolving
world.

3. Teaching Statistics for an Evolving World
The demands imposed by Data Science are transforming the way we teach
statistics at the undergraduate level, with several initiatives highlighted in the
previous section. But as Data Science continues to evolve, and its practice
continues to change so quickly, no program of study in Statistics or Data
Science can hope to teach all the knowledge and skills that students will need

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