Fundamentals of Data Science: Theory and Practice

English | 2023 | ISBN: 978-0323917780 | 334 Pages | PDF, EPUB | 38 MB

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.

• 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
• Provides updates on 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
• Covers computer program code for implementing descriptive and predictive algorithms