Fundamentals of data science : theory and practice
(eBook)
Author
Contributors
Published
London ; Academic Press, [2024].
Format
eBook
ISBN
0323972632, 9780323972635
Physical Desc
1 online resource
Status
Description
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Language
English
Notes
Bibliography
Includes bibliographical references and index.
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.
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Citations
APA Citation, 7th Edition (style guide)
Kalita, J. K., Bhattacharyya, D. K., & Roy, S. (. s. (2024). Fundamentals of data science: theory and practice . Academic Press.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Kalita, Jugal Kumar, Dhruba K., Bhattacharyya and Swarup (Computer scientist), Roy. 2024. Fundamentals of Data Science: Theory and Practice. Academic Press.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Kalita, Jugal Kumar, Dhruba K., Bhattacharyya and Swarup (Computer scientist), Roy. Fundamentals of Data Science: Theory and Practice Academic Press, 2024.
MLA Citation, 9th Edition (style guide)Kalita, Jugal Kumar,, Dhruba K. Bhattacharyya, and Swarup (Computer scientist) Roy. Fundamentals of Data Science: Theory and Practice Academic Press, 2024.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
b49e2ff6-a88f-a9c8-3594-daae7fa7f04a-eng
Grouping Information
| Grouped Work ID | b49e2ff6-a88f-a9c8-3594-daae7fa7f04a-eng |
|---|---|
| Full title | fundamentals of data science theory and practice |
| Author | kalita jugal kumar |
| Grouping Category | book |
| Last Update | 2024-09-06 16:31:08PM |
| Last Indexed | 2024-09-14 03:55:02AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Sep 11, 2024 |
| Last Used | Sep 11, 2024 |
Marc Record
| First Detected | Jul 29, 2024 04:08:48 PM |
|---|---|
| Last File Modification Time | Sep 06, 2024 04:55:01 PM |
MARC Record
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| 100 | 1 | |a Kalita, Jugal Kumar,|e author.|0 http://id.loc.gov/authorities/names/no2013110696 | |
| 245 | 1 | 0 | |a Fundamentals of data science :|b theory and practice /|c Jugal K. Kalita, Dhruba K. Bhattacharyya, Swarup Roy. |
| 264 | 1 | |a London ;|a San Diego, CA :|b Academic Press,|c [2024] | |
| 300 | |a 1 online resource | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front Cover -- Fundamentals of Data Science -- Copyright -- Contents -- Preface -- Acknowledgment -- Foreword -- Foreword -- 1 Introduction -- 1.1 Data, information, and knowledge -- 1.2 Data Science: the art of data exploration -- 1.2.1 Brief history -- 1.2.2 General pipeline -- 1.2.2.1 Data collection and integration -- 1.2.2.2 Data preparation -- 1.2.2.3 Learning-model construction -- 1.2.2.4 Knowledge interpretation and presentation -- 1.2.3 Multidisciplinary science -- 1.3 What is not Data Science? -- 1.4 Data Science tasks -- 1.4.1 Predictive Data Science | |
| 505 | 8 | |a 1.4.2 Descriptive Data Science -- 1.4.3 Diagnostic Data Science -- 1.4.4 Prescriptive Data Science -- 1.5 Data Science objectives -- 1.5.1 Hidden knowledge discovery -- 1.5.2 Prediction of likely outcomes -- 1.5.3 Grouping -- 1.5.4 Actionable information -- 1.6 Applications of Data Science -- 1.7 How to read the book? -- References -- 2 Data, sources, and generation -- 2.1 Introduction -- 2.2 Data attributes -- 2.2.1 Qualitative -- 2.2.1.1 Nominal -- 2.2.1.2 Binary -- 2.2.1.3 Ordinal -- 2.2.2 Quantitative -- 2.2.2.1 Discrete -- 2.2.2.2 Continuous -- 2.2.2.3 Interval -- 2.2.2.4 Ratio | |
| 505 | 8 | |a 2.3 Data-storage formats -- 2.3.1 Structured data -- 2.3.2 Unstructured data -- 2.3.3 Semistructured data -- 2.4 Data sources -- 2.4.1 Primary sources -- 2.4.2 Secondary sources -- 2.4.3 Popular data sources -- 2.4.4 Homogeneous vs. heterogeneous data sources -- 2.5 Data generation -- 2.5.1 Types of synthetic data -- 2.5.2 Data-generation steps -- 2.5.3 Generation methods -- 2.5.4 Tools for data generation -- 2.5.4.1 Software tools -- 2.5.4.2 Python libraries -- 2.6 Summary -- References -- 3 Data preparation -- 3.1 Introduction -- 3.2 Data cleaning -- 3.2.1 Handling missing values | |
| 505 | 8 | |a 3.2.1.1 Ignoring and discarding data -- 3.2.1.2 Parameter estimation -- 3.2.1.3 Imputation -- 3.2.2 Duplicate-data detection -- 3.2.2.1 Knowledge-based methods -- 3.2.2.2 ETL method -- 3.3 Data reduction -- 3.3.1 Parametric data reduction -- 3.3.2 Sampling -- 3.3.3 Dimensionality reduction -- 3.4 Data transformation -- 3.4.1 Discretization -- 3.4.1.1 Supervised discretization -- 3.4.1.2 Unsupervised discretization -- 3.5 Data normalization -- 3.5.1 Min-max normalization -- 3.5.2 Z-score normalization -- 3.5.3 Decimal-scaling normalization -- 3.5.4 Quantile normalization | |
| 505 | 8 | |a 3.5.5 Logarithmic normalization -- 3.6 Data integration -- 3.6.1 Consolidation -- 3.6.2 Federation -- 3.7 Summary -- References -- 4 Machine learning -- 4.1 Introduction -- 4.2 Machine Learning paradigms -- 4.2.1 Supervised learning -- 4.2.2 Unsupervised learning -- 4.2.3 Semisupervised learning -- 4.3 Inductive bias -- 4.4 Evaluating a classifier -- 4.4.1 Evaluation steps -- 4.4.1.1 Validation -- 4.4.1.2 Testing -- 4.4.1.3 K-fold crossvalidation -- 4.4.2 Handling unbalanced classes -- 4.4.3 Model generalization -- 4.4.3.1 Underfitting -- 4.4.3.2 Overfitting -- 4.4.3.3 Accurate fittings | |
| 520 | |a 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. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on February 20, 2024). | ||
| 650 | 0 | |a Big data.|0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 700 | 1 | |a Bhattacharyya, Dhruba K.,|e author.|0 http://id.loc.gov/authorities/names/no2007049521 | |
| 700 | 1 | |a Roy, Swarup|c (Computer scientist),|e author.|0 http://id.loc.gov/authorities/names/no2020076103 | |
| 856 | 4 | 0 | |u https://www.aclib.us/OReilly |