Fundamentals of Data Science: Theory and Practice

Fundamentals of Data Science: Theory and Practicepresents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers, and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining, and analytics. Data science is an evolving area of study that is extensively used in solving real-life problems. It is not just about machine learning, statistics, or databases. Instead, it is a comprehensive study of a number of topics that help extract novel knowledge from data, starting with preparing the data, applying suitable intelligent learning models, and interpreting the outcome. The models applied are not “one-size-fits-all” and vary with the nature of the data and the applications under consideration. The authors provide discussions of theoretical as well as practical approaches in data science, with a goal to produce a solid understanding of data science which ultimately leads to novel knowledge discovery. Fundamentals of Data Science: Theory and Practice presents the authors’ research experiences and achievements in data science applications. The approach of this book is distinct because of the following clearly enumerated characteristics: The book containsan in-depth discussion on topics that are essential for data science projects, including pre-processing, carried out before applying predictive and descriptive data analysis tasks, and proximity measures for numeric, categorical and mixed-type data, without the knowledge of which it is impossible to develop learning algorithms that apply to a wide range of domains and applications.

The authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, the authors present a number of descriptive learning tasks, including a dedicated chapter on predictive learning (or mining), as well as a wide range of applications,featuring Big Data mining as one of the emphasized topics. The authors discuss the strength and limitations of a number of methods for Big Data miningand also delve in-depth into ensemble learning techniques and analyze their pros and cons.