Description
Introduction I. Classical Statistics and Data Science 1. Ready Set Go 2. Classical Statistics 3. Data Science II. Dispelling Myth and Alternatives 4. Estimating Uncertainty 5. Avoiding Insignificance 6. Lessons from Regression 7. Bayesian Basics III. Programming and Data 8. Programming 9 Data Visualization 10. Big Data 11. Data Reduction IV. Machine Learning and Beyond 12. Supervised Machine Learning: Core Methods 13. Supervised Machine Learning: Hyperparameter Tuning 14. Supervised Machine Learning: Complex Effects 15. Unsupervised Machine Learning 16. Other Data Science Horizons Suggested Answers to Exercises References Author Index Subject Index About the Author
Rex B. Kline, PhD, is Professor of Psychology at Concordia University in MontrA (c)al, QuA (c)bec, Canada. Since earning a doctorate in clinical psychology, Dr. Kline has conducted research on the psychometric evaluation of cognitive abilities, behavioral and scholastic assessment of children, structural equation modeling, training of researchers, statistics reform in the behavioral sciences, and usability engineering in computer science. He has published a number of chapters, journal articles, and books in these areas.

