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.
1. 1. Introduction - p. 1 2. 2. Data, sources, and generation - p. 15 3. 3. Data preparation - p. 31 4. 4. Machine learning - p. 47 5. 5. Regression - p. 69 6. 6. Classification - p. 91 7. 7. Artificial neural networks - p. 121 8. 8. Feature selection - p. 161 9. 9. Cluster analysis - p. 181 10. 10. Ensemble learning - p. 215 11. 11. Association-rule mining - p. 233 12. 12. Big Data analysis - p. 259 13. 13. Data Science in practice - p. 277 14. 14. Conclusion - p. 295 15. Index - p. 297
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.
1. 1. Introduction - p. 1 2. 2. Data, sources, and generation - p. 15 3. 3. Data preparation - p. 31 4. 4. Machine learning - p. 47 5. 5. Regression - p. 69 6. 6. Classification - p. 91 7. 7. Artificial neural networks - p. 121 8. 8. Feature selection - p. 161 9. 9. Cluster analysis - p. 181 10. 10. Ensemble learning - p. 215 11. 11. Association-rule mining - p. 233 12. 12. Big Data analysis - p. 259 13. 13. Data Science in practice - p. 277 14. 14. Conclusion - p. 295 15. Index - p. 297
