Research and development in the general area of data science (DS) has grown
beyond anticipation in the past few years. The initial wave had focus on modeling
and tools drawn from various fields including mathematics, statistics, information
science, software engineering, signal processing, probability models, machine learning, data mining, database systems, pattern recognition, visualization, predictive analytics, uncertain modeling, data warehousing, artificial intelligence, and high performance computing. Many projects were defined, developed, and deployed in
isolation. In the second wave, general availability of data as well as techniques,
tools, libraries, and toolkits, and, more importantly, hardware and software systems
to support it collectively contributed to the growth of DS research. Today, we
are observing the development and application of complex DS projects in many
domains. We can find large-scale projects that require application of DS tools and
techniques in each step of the process, from data collection to deployment.
The goal of this volume is to provide application examples of applied DS to solve
complex science and engineering problems. In this volume, the contributing authors
provide examples and solutions in various engineering, business, bioinformatics,
geomatics, education, and environmental science. Through reviewing the contributions in this volume, the professionals and practitioners in each corresponding field
can possibly understand the benefits of DS in their domain and understand where a
particular theory, technique, or tool would be applied correctly and efficiently.