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Unique Contributions and Opportunities
of iSchools in Data Science Education
Han Xi
Zhu Qinghua
School of Information Management
Nanjing University
China
Definition of Data Science
• the study of the extraction of knowledge from data
• concerned with the collection, preparation, analysis,
visualization, management, and preservation of large
collections of information
• involves principles, processes, and techniques for
understanding phenomena via the analysis of data
• employs techniques and theories drawn from different
fields within the broad areas of mathematics, statistics
and information technology
Definition of Data Scientist
•
have to master the knowledge and the skills of data
mining, statistics, information technology, information
visualization and communication, as well as engineering
and computer science. They also should acquire
experiences in the specific research or industry domain of
their future work and specialization.
• need to integrate structured and unstructured data
sources, apply analytics beyond traditional data
processing (data mining, machine learning, and artificial
neural networks), and communicate the analysis to
audience
Four Contributions and Opportunities
1
2
3
4
Data Thinking
Data Management
Absorptive Capacity of Information
Technology
Interdisciplinary Attributes
Data Thinking
•
Data thinking is the generic mental pattern observed
during the processes of picking a subject to start with,
identifying its parts or components, organizing and
describing them in an informative fashion that is relevant
to what motivated and initiated the whole processes.
•
Data thinking is even more vital,just like culture
Data Management
•
From a more macroscopic perspective, the activity
throughout data life-cycle is data management, including
data collection, preservation, organization, analysis and
the dissemination of results for data-based decision
making.
Absorptive Capacity of Information Technology
• information science cannot surpass the computer science
on information technology frontier research and
development, it is effective in absorbing information
technology and applying it into daily practice.
• there are no other disciplines that pay more attention to the
application of information technology to solve problem
than information science.
Interdisciplinary Attributes
• Data science requires not only information technology, data
management capabilities and statistical methods, but also
inter-disciplinary knowledge to solve various questions in
different backgrounds. It is a broad and interdisciplinary field
What should we do?
• The reform of information science education towards data
science education is necessary and urgent.
• We need to introduce more topics such as machine
learning, programming, predictive modeling and statistics
methods and tools that are currently closely associated
with data science.
• We should focus on data analytics and conduct different
kinds of programs to train specialists