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: "Unlock the next level of data science proficiency with Volume 4 of Patrick Boily's pioneering 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 that redefine the boundaries of data science.
Step into the realm of queueing systems and explore the probabilistic nature of waiting lines, venture into the Bayesian universe to revolutionize your understanding of statistical inference, then dive into real-time challenges of mining data streams and unveil the latent structures in network data, aided by insights from a diverse team of experts. From the enigmatic world of anomaly detection to the intricate web of text analysis and mining, this volume will help you broaden your analytical horizons.
Like its predecessors, the fourth volume upholds the series' tool-agnostic philosophy, reinforcing the 'Understanding' that serves as its cornerstone. Whether you are an experienced data scientist, a researcher in the making, or a seasoned professional looking to stay ahead of the curve, Techniques of Data Analysis serves as both a comprehensive manual and a treasury of advanced analytical methods.
Designed for effective parallel reading with guided lectures or as a standalone resource for deep independent study, this volume builds seamlessly upon the foundations established by the previous volumes, equipping you with the advanced skills you need to meet the sophisticated challenges of contemporary data science."