Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application.
This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems.
1. Part I: Introduction - p. 1 2. 1. The background and history of predictive analytics - p. 3 3. 2. Theoretical considerations for data analytics - p. 23 4. 3. The data analytics process - p. 35 5. 4. Tools - p. 45 6. Part II: Data preparation - p. 55 7. 5. Data access - p. 57 8. 6. Data Understanding - p. 69 9. 7. Visualizing data for understanding - p. 75 10. 8. Data cleaning - p. 85 11. 9. Data conditioning - p. 95 12. 10. Data and feature engineering - p. 103 13. 11. Feature selection - p. 117 14. 12. A data preparation cookbook - p. 125 15. Part III: Modeling - p. 135 16. 13. A taxonomy of algorithms - p. 137 17. 14. Modeling methods - p. 149 18. 15. Model evaluation and enhancement - p. 169 19. 16. Ensembles and complexity: a practical approach - p. 183 20. 17. Deep learning versus traditional machine learning - p. 193 21. 18. Interpretable machine learning and explainable AI - p. 201 22. 19. Human in the loop machine learning and data annotation - p. 211 23. Part IV: Applications - p. 221 24. 20. Overview of healthcare delivery and medical informatics - p. 223 25. 21. Customer response modeling with temporal abstractions - p. 231 26. 22. Academic Analytics: a case study in student retention - p. 241 27. 23. Medical informatics case study: predicting type 1 precancerous colon polyps - p. 249 28. 24. Credit risk case study - p. 259 29. 25. The intelligence revolution - p. 265 30. Part V: Right models – luck & ethics of analytics - p. 273 31. 26. The “Right Model” for the “Right Purpose” – when less is good enough - p. 275 32. 27. Ethics and data analytics - p. 281 33. 28. The significance of luck - p. 287 34. Part VI: Tutorials and case studies - p. 291 35. Tutorial A. Example of data mining recipes using Windows 10 and STATISTICA 13 - p. 293 36. Tutorial B. Analysis of Hurricane data (Hurrdata.sta) using the STATISTICA Data Miner 13 - p. 303 37. Tutorial C. Case study – Predicting student success at high-stakes nursing examinations (NCLEX) using IBM SPSS Modeler and STATISTICA Data Miner 13 - p. 317 38. Tutorial D. Constructing a Histogram using MidWest Company Personality Data with KNIME - p. 339 39. Tutorial E. Feature selection using KNIME - p. 353 40. Tutorial F. Medical/business tutorial using STATISTICA Data Miner 13 determining possible predictors for days with hospice service for patients with dementia - p. 367 41. Tutorial G. Modeling hospice residence stay with KNIME - p. 393 42. Tutorial H. Data preparation: Merging data sources - p. 401 43. Tutorial I. Data description - p. 413 44. Tutorial J. Data cleaning and recoding - p. 419 45. Tutorial K. Dummy coding category variables - p. 427 46. Tutorial L. Outlier handling - p. 435 47. Tutorial M. Filling missing values with constants - p. 441 48. Tutorial N. Filling missing values with formulas - p. 445 49. Tutorial O. Filling missing values with a model - p. 449 50. APPENDIX A. Listing of tutorials and other resources on this book’s companion web page - p. 459 51. Appendix B - p. 463 52. Index - p. 465
Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application.
This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems.
1. Part I: Introduction - p. 1 2. 1. The background and history of predictive analytics - p. 3 3. 2. Theoretical considerations for data analytics - p. 23 4. 3. The data analytics process - p. 35 5. 4. Tools - p. 45 6. Part II: Data preparation - p. 55 7. 5. Data access - p. 57 8. 6. Data Understanding - p. 69 9. 7. Visualizing data for understanding - p. 75 10. 8. Data cleaning - p. 85 11. 9. Data conditioning - p. 95 12. 10. Data and feature engineering - p. 103 13. 11. Feature selection - p. 117 14. 12. A data preparation cookbook - p. 125 15. Part III: Modeling - p. 135 16. 13. A taxonomy of algorithms - p. 137 17. 14. Modeling methods - p. 149 18. 15. Model evaluation and enhancement - p. 169 19. 16. Ensembles and complexity: a practical approach - p. 183 20. 17. Deep learning versus traditional machine learning - p. 193 21. 18. Interpretable machine learning and explainable AI - p. 201 22. 19. Human in the loop machine learning and data annotation - p. 211 23. Part IV: Applications - p. 221 24. 20. Overview of healthcare delivery and medical informatics - p. 223 25. 21. Customer response modeling with temporal abstractions - p. 231 26. 22. Academic Analytics: a case study in student retention - p. 241 27. 23. Medical informatics case study: predicting type 1 precancerous colon polyps - p. 249 28. 24. Credit risk case study - p. 259 29. 25. The intelligence revolution - p. 265 30. Part V: Right models – luck & ethics of analytics - p. 273 31. 26. The “Right Model” for the “Right Purpose” – when less is good enough - p. 275 32. 27. Ethics and data analytics - p. 281 33. 28. The significance of luck - p. 287 34. Part VI: Tutorials and case studies - p. 291 35. Tutorial A. Example of data mining recipes using Windows 10 and STATISTICA 13 - p. 293 36. Tutorial B. Analysis of Hurricane data (Hurrdata.sta) using the STATISTICA Data Miner 13 - p. 303 37. Tutorial C. Case study – Predicting student success at high-stakes nursing examinations (NCLEX) using IBM SPSS Modeler and STATISTICA Data Miner 13 - p. 317 38. Tutorial D. Constructing a Histogram using MidWest Company Personality Data with KNIME - p. 339 39. Tutorial E. Feature selection using KNIME - p. 353 40. Tutorial F. Medical/business tutorial using STATISTICA Data Miner 13 determining possible predictors for days with hospice service for patients with dementia - p. 367 41. Tutorial G. Modeling hospice residence stay with KNIME - p. 393 42. Tutorial H. Data preparation: Merging data sources - p. 401 43. Tutorial I. Data description - p. 413 44. Tutorial J. Data cleaning and recoding - p. 419 45. Tutorial K. Dummy coding category variables - p. 427 46. Tutorial L. Outlier handling - p. 435 47. Tutorial M. Filling missing values with constants - p. 441 48. Tutorial N. Filling missing values with formulas - p. 445 49. Tutorial O. Filling missing values with a model - p. 449 50. APPENDIX A. Listing of tutorials and other resources on this book’s companion web page - p. 459 51. Appendix B - p. 463 52. Index - p. 465
