Name: Fundamentals of Data Science: Theory and Practice (en Inglés)
SKU: 56345935
Price: 146.86 USD
Availability: InStock
Author: Kalita, Jugal K. ; Bhattacharyya, Dhruba K. ; Roy, Swarup
ISBN: 9780323917780

portada Fundamentals of Data Science: Theory and Practice (en Inglés)

Fundamentals of Data Science: Theory and Practice (en Inglés)

Formato

Libro Físico

Autor

Jugal K. Kalita

Dhruba K. Bhattacharyya

Swarup Roy

Editorial

Academic Press

Idioma

Inglés

N° páginas

334

Encuadernación

Tapa Blanda

Dimensiones

23x15.2x3.4 cm

Peso

1.10 kg.

ISBN13

9780323917780

Categorías

Matemáticas discretas
Matemáticas aplicadas
Ciencias de la computación

Fundamentals of Data Science: Theory and Practice (en Inglés)

Jugal K. Kalita (Autor) · Dhruba K. Bhattacharyya (Autor) · Swarup Roy (Autor) · Academic Press · Tapa Blanda

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Reseña del libro "Fundamentals of Data Science: Theory and Practice (en Inglés)"

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