Role of Data Science in Education

Abstract

The field of data science has witnessed a surge
in prominence across academia and industry. However,
there remains a lack of formal consensus on how best to
teach it. Currently, many instructors of data science are
practitioners themselves, including computational
researchers in academia and data scientists in industry. To
gain insights into how these practitioner-instructors
impart their knowledge to novices and how this compares
to teaching more traditional programming, we conducted
interviews with 20 data scientists who teach in various
settings, ranging from small-group workshops to large
online courses. Our findings reveal several key insights:
1) Practitioner-instructors must empathize with a diverse
range of student backgrounds and expectations,
highlighting the importance of tailoring teaching methods
to accommodate varying levels of prior knowledge and
learning styles.
2) They emphasize teaching technical workflows that
integrate authentic practices related to coding, data
manipulation, and effective communication. This
approach ensures that students gain practical, real-world
skills that are directly applicable in professional settings.
3) These instructors encounter challenges related to
balancing authenticity and abstraction in software setup,
sourcing and curating pedagogically-relevant datasets,
and helping students navigate the inherent uncertainties
of data analysis. Addressing these challenges is crucial for
fostering a deeper understanding of data science concepts
and methodologies.
Overall, our findings provide valuable insights that can
inform the development of improved tools and
methodologies for data science education. By addressing
the unique challenges faced by practitioner-instructors
and leveraging their expertise, we can work towards
expanding data literacy and empowering more
individuals to harness the power of data science in their
respective fields.