Fundamentals of Data Science

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. Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning Includes key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis Includes computer programcode for implementing descriptive and predictive algorithms