http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Fundamentals of Data Science: Theory and Practice presents 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. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data.The book's 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, a number of descriptive learning tasks are included.
1 Introduction -- 2 Data, sources, and generation -- 3 Data preparation -- 4 Machine learning -- 5 Regression -- 6 Classification -- 7 Artificial neural networks -- 8 Feature Selection -- 9 Cluster analysis -- 10 Ensemble learning -- 11 Association-rule mining -- Big data analysis -- 13 Data science in practice -- 14 Conclusion
Jugal K. Kalita, Dhruba K. Bhattacharyya, Swarup Roy.
Includes bibliographical references and index.