The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understandin

Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruct

Data encountered in a computer program is classified by type. Common types include integers, floating point numbers, Boolean values, and characters. Data structures are a means of aggregating many of these scalar values into a larger collection of values. An algorithm is an explicit sequence of instructions, performed on data, to accomplish a desired objective. This open access book serv

If you're a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering - data structures and algorithms - in a way that's clearer, more concise, and more engaging than other materials. By emphasizing practical knowledge and skills over theory, aut

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data cru

This book provides implementations of common and uncommon algorithms in pseudocode which is language independent and provides for easy porting to most imperative programming languages. It is not a definitive book on the theory of data structures and algorithms. For the most part this book presents implementations devised by the authors themselves based on the concepts by which the respec

This open book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithm

Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times - a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This rev

Data Mesh is a relatively new approach to data management. It combines several important trends in data management, including domain-driven design and data as a product, to decentralize the ownership of ingestion, processing, and serving of data. Zhamak Dehghani defined the term in 2019 as "a decentralized sociotechnical approach to share, access, and manage analytical data in complex an

Learn how to accelerate C++ programs using data parallelism. This open book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern