Main 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.

Contents:

1. Introduction 2. Data, Quality and Pre-processing 3. Proximity and Validation 4. Predictive Data Science 5. Descriptive Data Science 6. Ensemble Learning 7. Association Rule Mining 8. Handling Big Data 9. Incremental and Distributed Learning 10. Data Science Practice and Trends 11. Conclusion