eBook - ePub
Fundamentals of Data Science
Theory and Practice
- 200 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
About this book
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
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Yes, you can access Fundamentals of Data Science by Jugal K. Kalita,Dhruba K. Bhattacharyya,Swarup Roy in PDF and/or ePUB format, as well as other popular books in Mathematics & Computer Science General. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- Acknowledgment
- Foreword
- Foreword
- 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
- 12 Big Data analysis
- 13 Data Science in practice
- 14 Conclusion
- Index