Unleashing the Problem-Solving Potential of
Next-Generation Data Scientists
Lizhen Zhu, James Z. Wang
The Pennsylvania State University, University Park, USA
Abstract:
Data science, an emerging multidisciplinary
field, resides at the intersection of computational sciences,
statistical modeling, and domain-specific sciences. The current norm
for data science education predominantly focuses on graduate programs,
which presume a pre-existing knowledge base in one or more relevant
sciences. However, this framework often overlooks those who don't plan
to pursue graduate studies, thereby limiting their exposure to this
rapidly expanding field. Penn State addressed this gap by establishing
one of the first undergraduate degree programs in Data Sciences, a
collaboration between the College of Information Sciences and
Technology, the Department of Computer Science and Engineering, and
the Department of Statistics. One key component of this program is a
project-focused, writing-intensive course designed for upper-class
undergraduates. This course guides students through the entire data
science project pipeline, from problem identification to solution
presentation. It allows students to apply foundational data science
principles to real-world problems, advancing their understanding
through practical application. This chapter details the objectives,
rationale, and course design, alongside reflections from our teaching
experience. The insights provided could be helpful to instructors
developing similar data science programs or courses at an
undergraduate level, broadening the influence of this important field.
Full Paper
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More information
Citation:
Lizhen Zhu and James Z. Wang, ``Unleashing the Problem-Solving
Potential of Next-Generation Data Scientists,'' Innovative Practices
in Teaching Information Sciences and Technology, Volume 2, John
M. Carroll (editor), Springer, Chapter ??, 23 pages, 2024.
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Last Modified:
July 27, 2023
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