xx pages | July 2024 | Quadrangle
Preface | Contents | Index | Datasets
DUDADS Volumes: [1 | 2 | 3 | 4 | 5 | PDV]
E. Ghashim and P. Boily
Chapter 25: Bayesian Data Analysis (50 pages)
P. Boily and E. Ghashim
Chapter 26: Anomaly Detection and Outlier Analysis (60 pages)
P. Boily (with contributions from Y. Cissokho, A. Macfie, S. Fadel, and R. Millson)
Chapter 27: Text Analysis and Text Mining (118 pages)
P. Boily (with contributions from A. Macfie)
Chapter 28: Mining Data Streams (xx pages)
K. Cheung and P. Boily
Chapter 29: Network Data Analysis (xx pages)
P. Boily (with contributions from L. Haque and K. Park)
Back Cover Description: "The next level of data science proficiency is yours to unlock with Volume 4 of Patrick Boily's comprehensive series, Data Understanding, Data Analysis, and Data Science. Techniques of Data Analysis takes you beyond the basics, offering an in-depth exploration of specialized methodologies and approaches at the boundaries of data science.
This volume will help you broaden your analytical horizons through the study of queueing systems and the probabilistic nature of waiting lines, the Bayesian universe which will revolutionize your understanding of statistical inference, the real-time challenges of mining data streams, the latent structures in network data, the oddities of anomaly detection, and the intricate dance of text mining and sentiment analysis,
Like its predecessors, the fourth volume upholds the series' tool-agnostic philosophy, reinforcing the 'Understanding' that serves as its cornerstone. Experienced data scientists, researchers in the making, and seasoned professionals looking to stay ahead of the curve will benefit from Techniques of Data Analysis, which serves both as a comprehensive manual and as a treasury of advanced analytical methods.
Designed for effective parallel reading with guided lectures or as a standalone resource for independent study, this volume builds seamlessly upon the foundations established by the previous volumes, providing readers with the advanced skills needed to meet the sophisticated challenges of contemporary data science."