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- Author: Jugal K Kalita
- Publisher: Academic Press
- ISBN-10: 032391778X
- ISBN-13: 9780323917780
- Format: 15.2 x 23 x 3.4 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
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- Author: Jugal K Kalita
- Publisher: Academic Press
- ISBN-10: 032391778X
- ISBN-13: 9780323917780
- Format: 15.2 x 23 x 3.4 cm, softcover
- Language: English English
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.
Reviews