Computer Science: Books and Ebooks

*Available at EBSCOhost*
This book systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years experience in teaching and practicing data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practice of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets.

*Available at EBSCOhost*
This book presents original results on the leading edge of computer science research. Chapter One discusses motion planning for robots with rigid bodies. Chapter Two provides an overview of handheld/mobile computing devices, pathology applications, social media in the context of pathology, and novel mobile-driven technologies like wearables and mixed reality tech, and discusses their potential impact in pathology. Chapter Three focuses on reliability and accessibility issues of a distributed cloud service based on Semi-Markov modeling. Chapter Four examines parallel dynamic programming for large-scale date applications. Chapter Five presents the ensemble of both texture and color features for reliable person re-identification. Chapter Six provides an overview of common techniques and commercial products used in tracking people and objects within indoor environments. Chapter Seven analyzes quadratic programming formulation for controlling the emissions of air pollution point sources.

*Available at EBSCOhost*
Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics and mining data to reveal hidden patterns and trends.You will then use the matplotlib library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before finally performing k-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.