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Data Structures I Essentials
by Dennis SmolarskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures I includes scalar variables, arrays and records, elementary sorting, searching, linked lists, queues, and appendices of binary notation and subprogram parameter passing.
Data Structures II Essentials
by Dennis C. SmolarskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures II includes sets, trees, advanced sorting, elementary graph theory, hashing, memory management and garbage collection, and appendices on recursion vs. iteration, algebraic notation, and large integer arithmetic.
Data Structures and Abstractions with Java (Fourth Edition)
by Frank M. Carrano Timothy M. HenryA book for an introductory course in data structures, typically known as CS-2.
Data Structures and Algorithm Analysis in C++, Third Edition
by Dr Clifford A. ShafferWith its focus on creating efficient data structures and algorithms, this comprehensive text helps readers understand how to select or design the tools that will best solve specific problems. It uses Microsoft C++ as the programming language and is suitable for second-year data structure courses and computer science courses in algorithm analysis.Techniques for representing data are presented within the context of assessing costs and benefits, promoting an understanding of the principles of algorithm analysis and the effects of a chosen physical medium. The text also explores tradeoff issues, familiarizes readers with the most commonly used data structures and their algorithms, and discusses matching appropriate data structures to applications. The author offers explicit coverage of design patterns encountered in the course of programming the book's basic data structures and algorithms. Numerous examples appear throughout the text.
Data Structures and Algorithm Analysis in Java, Third Edition
by Dr Clifford A. ShafferWith its focus on creating efficient data structures and algorithms, this comprehensive text helps readers understand how to select or design the tools that will best solve specific problems. It uses Java as the programming language and is suitable for second-year data structure courses and computer science courses in algorithm analysis. Techniques for representing data are presented within the context of assessing costs and benefits, promoting an understanding of the principles of algorithm analysis and the effects of a chosen physical medium. The text also explores tradeoff issues, familiarizes readers with the most commonly used data structures and their algorithms, and discusses matching appropriate data structures to applications. The author offers explicit coverage of design patterns encountered in the course of programming the book's basic data structures and algorithms. Numerous examples appear throughout the text.
Data Structures and Algorithms Using C#
by Michael McmillanC# programmers: no more translating data structures from C++ or Java to use in your programs! Mike McMillan provides a tutorial on how to use data structures and algorithms plus the first comprehensive reference for C# implementation of data structures and algorithms found in the . NET Framework library, as well as those developed by the programmer. The approach is very practical, using timing tests rather than Big O notation to analyze the efficiency of an approach. Coverage includes arrays and array lists, linked lists, hash tables, dictionaries, trees, graphs, and sorting and searching algorithms, as well as more advanced algorithms such as probabilistic algorithms and dynamic programming. This is the perfect resource for C# professionals and students alike.
Data Structures and Algorithms in JavaScript
by Federico KerekiNot the Same Old JavaScript.Think you know JavaScript? Think again. This isn&’t your typical coding book—it&’s a deep dive into the powerful world of data structures and algorithms that will transform the way you approach problem solving in JavaScript.Whether you&’re a frontend developer tackling complex applications, a backend engineer building scalable systems, or a programmer preparing for technical interviews, this book will revolutionize the way you code. Key features include:Modern JavaScript techniques: Use the latest language features and functional programming principles for cleaner, more efficient code.Performance-focused approach: Analyze and optimize algorithms using Big O notation.Essential algorithms explained: Implement and fine-tune core algorithms like quicksort, merge sort, digital search, and binary search.Algorithm design strategies: Solve challenging problems with techniques like recursion, dynamic programming, backtracking, and brute-force search.Advanced data structures: Explore complex structures such as binary search trees, heaps, and graphs. Each chapter is carefully crafted with clear, no-nonsense explanations of complex concepts, real-world coding examples, and challenging questions (with answers at the end) to reinforce your understanding.Ready to break free from ordinary JavaScript? Whether your aim is to build cutting-edge web applications, optimize critical systems, or land your dream job, this book equips you with the advanced JavaScript knowledge that sets true experts apart.
Data Structures and Algorithms in Swift: Implement Stacks, Queues, Dictionaries, and Lists in Your Apps
by Elshad KarimovControl the performance and stability of the apps you develop in Swift by working with and understanding advanced concepts in data structures and algorithms. All professional developers have to know which data structure and algorithms to use in their development process. Your choice directly affects the performance of your application. With this book, you’ll increase the performance of your software, become a better developer, and even pass tricky interview questions better when looking at professional development opportunities. Guided by compact and practical chapters, you'll learn the nature and proper use of data structures such as arrays, dictionaries, sets, stacks, queues, lists, hash tables, trie, heaps, binary trees, red black trees, and R-trees. Use the main differences among them to determine which will make your applications efficient and faster. Then tackle algorithms. Work with Big O notation; sorting algorithms such as Insertion, Merge, and Quick; Naive and Rabin Karp algorithms; and Graph Algorithms. Data Structures and Algorithms in Swift encourages you to further and understand how to best choose the perfect algorithm for your application’s needs. What You'll LearnRetrieve, add, and remove elements in arraysImplement stacks, queues, and lists in your appsSort algorithms and choose the best ones for your appsWho This Book Is ForDevelopers who have intermediate knowledge in Swift and want to improve their code performance and pass more complex interviews
Data Structures and Algorithms with JavaScript: Bringing classic computing approaches to the Web
by Michael McMillanAs an experienced JavaScript developer moving to server-side programming, you need to implement classic data structures and algorithms associated with conventional object-oriented languages like C# and Java. This practical guide shows you how to work hands-on with a variety of storage mechanisms—including linked lists, stacks, queues, and graphs—within the constraints of the JavaScript environment.Determine which data structures and algorithms are most appropriate for the problems you’re trying to solve, and understand the tradeoffs when using them in a JavaScript program. An overview of the JavaScript features used throughout the book is also included.This book covers:Arrays and lists: the most common data structuresStacks and queues: more complex list-like data structuresLinked lists: how they overcome the shortcomings of arraysDictionaries: storing data as key-value pairsHashing: good for quick insertion and retrievalSets: useful for storing unique elements that appear only onceBinary Trees: storing data in a hierarchical mannerGraphs and graph algorithms: ideal for modeling networksAlgorithms: including those that help you sort or search dataAdvanced algorithms: dynamic programming and greedy algorithms
Data Structures and Algorithms with Python (Undergraduate Topics in Computer Science)
by Kent D. Lee Steve HubbardThis textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.
Data Structures and Algorithms with Python: With an Introduction to Multiprocessing (Undergraduate Topics in Computer Science)
by Kent D. Lee Steve HubbardThis textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms—supported by motivating examples—that bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.Topics and features:Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective coursesProvides learning goals, review questions, and programming exercises in each chapter, as well as numerous examplesPresents a primer on Python for those coming from a different language backgroundAdds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial)Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithmsOffers downloadable programs and supplementary files at an associated website to help studentsStudents of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.Dr. Kent D. Lee is a Professor Emeritus of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer books, Python Programming Fundamentals, and Foundations of Programming Languages.Dr. Steve Hubbard is a Professor Emeritus of Mathematics and Computer Science at Luther College.
Data Structures for Engineers and Scientists Using Python
by Nishu Gupta Rakesh NayakThe text covers the fundamentals of Python programming and the implementation of data structures using Python programming with the help of worked-out examples. It provides a learning tool for engineers as well as for researchers and scientists of advanced level. The text further discusses important concepts such as polynomial manipulation, sparse matrices, implementation of stack using the queue model and topological sorting.This book: Discusses the implementation of various data structures such as an array, stack, queue, tree and graph along with sorting and searching algorithms. Includes programming tips to highlight important concepts and help readers avoid common programming errors. Presents each concept of data structure with a different approach and implements the same using Python programming. Offers rich chapter-end pedagogy including objective-type questions (with answers), review questions and programming exercises to facilitate review. Covers fundamentals of Python up to object-oriented concepts including regular expression. It is primarily written for senior undergraduate, graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering and information technology.
Data Structures in Depth Using C++: A Comprehensive Guide to Data Structure Implementation and Optimization in C++
by Mahmmoud MahdiUnderstand and implement data structures and bridge the gap between theory and application. This book covers a wide range of data structures, from basic arrays and linked lists to advanced trees and graphs, providing readers with in-depth insights into their implementation and optimization in C++. You’ll explore crucial topics to optimize performance and enhance their careers in software development. In today's environment of growing complexity and problem scale, a profound grasp of C++ data structures, including efficient data handling and storage, is more relevant than ever. This book introduces fundamental principles of data structures and design, progressing to essential concepts for high-performance application. Finally, you’ll explore the application of data structures in real-world scenarios, including case studies and use in machine learning and big data. This practical, step-by-step approach, featuring numerous code examples, performance analysis and best practices, is written with a wide range of C++ programmers in mind. So, if you’re looking to solve complex data structure problems using C++, this book is your complete guide. What You Will Learn Write robust and efficient C++ code. Apply data structures in real-world scenarios. Transition from basic to advanced data structures Understand best practices and performance analysis. Design a flexible and efficient data structure library. Who This Book is For Software developers and engineers seeking to deepen their knowledge of data structures and enhanced coding efficiency, and ideal for those with a foundational understanding of C++ syntax. Secondary audiences include entry-level programmers seeking deeper dive into data structures, enhancing their skills, and preparing them for more advanced programming tasks. Finally, computer science students or programmers aiming to transition to C++ may find value in this book.
Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples
by Jeremy KubicaLearn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures — a critical component in any programming endeavor. You&’ll learn how to work with more than 15 key data structures, from stacks, queues, and caches to bloom filters, skip lists, and graphs. You&’ll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and Quadtrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.
Data Structures using C: A Practical Approach for Beginners
by Amol M. Jagtap Ajit S. MaliThe data structure is a set of specially organized data elements and functions, which are defined to store, retrieve, remove and search for individual data elements. Data Structures using C: A Practical Approach for Beginners covers all issues related to the amount of storage needed, the amount of time required to process the data, data representation of the primary memory and operations carried out with such data. Data Structures using C: A Practical Approach for Beginners book will help students learn data structure and algorithms in a focused way. Resolves linear and nonlinear data structures in C language using the algorithm, diagrammatically and its time and space complexity analysis Covers interview questions and MCQs on all topics of campus readiness Identifies possible solutions to each problem Includes real-life and computational applications of linear and nonlinear data structures This book is primarily aimed at undergraduates and graduates of computer science and information technology. Students of all engineering disciplines will also find this book useful.
Data Teams: A Unified Management Model for Successful Data-Focused Teams
by Jesse AndersonLearn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. What You Will Learn Discover the three teams that you will need to be successful with big dataUnderstand what a data scientist is and what a data science team doesUnderstand what a data engineer is and what a data engineering team doesUnderstand what an operations engineer is and what an operations team doesKnow how the teams and titles differ and why you need all three teamsRecognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects Who This Book Is ForManagement, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects which may be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place. This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.
Data Technology in Materials Modelling (SpringerBriefs in Applied Sciences and Technology)
by Martin Thomas Horsch Silvia Chiacchiera Welchy Leite Cavalcanti Björn SchemberaThis open access book discusses advances in semantic interoperability for materials modelling, aiming at integrating data obtained from different methods and sources into common frameworks, and facilitating the development of platforms where simulation services in computational molecular engineering can be provided as well as coupled and linked to each other in a standardized and reliable way. The Virtual Materials Marketplace (VIMMP), which is open to all service providers and clients, provides a framework for offering and accessing such services, assisting the uptake of novel modelling and simulation approaches by SMEs, consultants, and industrial R&D end users. Semantic assets presented include the EngMeta metadata schema for research data infrastructures in simulation-based engineering and the collection of ontologies from VIMMP, including the ontology for simulation, modelling, and optimization (OSMO) and the VIMMP software ontology (VISO).
Data Thieves in Action: Examining the International Market for Stolen Personal Information (Palgrave Studies in Cybercrime and Cybersecurity)
by Thomas J. Holt Olga Smirnova Yi-Ting ChuaThis book examines the practices of cybercriminals who steal and sell personal information acquired through various means, including mass data breaches, to engage in cybercrime and fraud. Using data from multiple English and Russian language web forums, the authors identify the range of products sold in these active on-line marketplaces and the prospective profits earned by these actors. The social organization of these markets is analysed using sociological theory to understand the sophistication of the markets. Social network analyses of the relational networks of participants are also utilised to examine their sophistication and structure. In doing so, this work will contribute to the development of cybercrime studies, and will appeal to both social and computer scientists alike with an interest in the human aspects of cybercrime.
Data Usability in the Enterprise: How Usability Leads to Optimal Digital Experiences
by Praveen GujarEnsuring data usability is paramount to unlocking a company’s full potential and driving informed decision-making. Part of author Saurav Bhattacharya’s trilogy that covers the essential pillars of digital ecosystems—security, reliability, and usability—this book offers a comprehensive exploration of the fundamental concepts, principles, and practices essential for enhancing data accessibility and effectiveness. You’ll study the core aspects of data design, standardization, and interoperability, gaining the knowledge needed to create and maintain high-quality data environments. By examining the tools and technologies that improve data usability, along with best practices for data visualization and user-centric strategies, this book serves as an invaluable resource for professionals seeking to leverage data more effectively. The book also addresses crucial governance issues, ensuring data quality, integrity, and security are maintained. Through a detailed analysis of data governance frameworks and privacy concerns, you’ll see how to manage data responsibly. Additionally, the book includes compelling case studies that highlight successful data usability implementations, future trends, and the challenges faced in achieving optimal data usability. By fostering a culture of data literacy and usability, this book will help you and your organization navigate the evolving data landscape and harness the power of data for innovation and growth. What You Will Learn Understand the fundamental concepts and importance of data usability, including effective data design, enhancing data accessibility, and ensuring data standardization and interoperability. Review the latest tools and technologies that enhance data usability, best practices for data visualization, and strategies for implementing user-centric data approaches. Ensure data quality and integrity, while navigating data privacy and security concerns. Implement robust data governance frameworks to manage data responsibly and effectively. Who This Book Is For Cybersecurity and IT professionals
Data Visualization For Dummies
by Stephanie Diamond Mico YukA straightforward, full-color guide to showcasing data so your audience can see what you mean, not just read about itBig data is big news! Every company, industry, not-for-profit, and government agency wants and needs to analyze and leverage datasets that can quickly become ponderously large. Data visualization software enables different industries to present information in ways that are memorable and relevant to their mission. This full-color guide introduces you to a variety of ways to handle and synthesize data in much more interesting ways than mere columns and rows of numbers. Learn meaningful ways to show trending and relationships, how to convey complex data in a clear, concise diagram, ways to create eye-catching visualizations, and much more!Effective data analysis involves learning how to synthesize data, especially big data, into a story and present that story in a way that resonates with the audienceThis full-color guide shows you how to analyze large amounts of data, communicate complex data in a meaningful way, and quickly slice data into various viewsExplains how to automate redundant reporting and analyses, create eye-catching visualizations, and use statistical graphics and thematic cartographyEnables you to present vast amounts of data in ways that won't overwhelm your audiencePart technical manual and part analytical guidebook, Data Visualization For Dummies is the perfect tool for transforming dull tables and charts into high-impact visuals your audience will notice...and remember.
Data Visualization Made Simple: Insights into Becoming Visual
by Kristen SosulskiData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.
Data Visualization and Knowledge Engineering: Spotting Data Points with Artificial Intelligence (Lecture Notes on Data Engineering and Communications Technologies #32)
by Jude Hemanth Madhulika Bhatia Oana GemanThis book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns. A variety of scalable data visualization techniques are required to deal with constantly increasing volume of data in different formats. Knowledge engineering deals with the simulation of the exchange of ideas and the development of smart information systems in which reasoning and knowledge play an important role. Presenting research in areas like data visualization and knowledge engineering, this book is a valuable resource for students, scholars and researchers in the field. Each chapter is self-contained and offers an in-depth analysis of real-world applications. It discusses topics including (but not limited to) spatial data visualization; biomedical visualization and applications; image/video summarization and visualization; perception and cognition in visualization; visualization taxonomies and models; abstract data visualization; information and graph visualization; knowledge engineering; human–machine cooperation; metamodeling; natural language processing; architectures of database, expert and knowledge-based systems; knowledge acquisition methods; applications, case studies and management issues: data administration issues and knowledge; tools for specifying and developing data and knowledge bases using tools based on communication aspects involved in implementing, designing and using KBSs in cyberspace; Semantic Web.
Data Visualization and Storytelling with Tableau (Innovations in Multimedia, Virtual Reality and Augmentation)
by Mamta Mittal Nidhi Grover RahejaTableau, one of the most widely used visualization tools, helps in illustrating the ideas of data visualization and storytelling. Through Tableau’s Data Visualization and Storytelling feature, aspiring data scientists and analysts can develop their visual analytics skills and use them in both academic and business contexts.Data Visualization and Storytelling with Tableau enables budding data analysts and data scientists to develop and sharpen their skills in the field of visual analytics and apply them in business scenarios as well as in academic context. This book approaches the Data Visualization workflow from a practical point of view, emphasizing the steps involved and the outcomes attained. A major focus of this book is the application and deployment of real-time case studies. Later chapters in this book provide comprehensive coverage for advanced topics such as data storytelling, data insights, color selection in graphs, publishing in tableau public, and misleading visualizations. Thus, this book emphasizes the need to visually examine and evaluate data through stories and interactive dashboards that are made up of appropriate graphs and charts. The case studies covered in this book are a natural extension of the visualization topics that are covered in each chapter. The intention is to empower readers to generate various dashboards, stories, graphs, charts, and maps to visualize and analyze data and support decision-making in business. Advanced charts that are pertinent to project management operations are also thoroughly explored, including comparison charts, distribution charts, composition charts, and maps. All these concepts will lay a solid foundation for data visualization applications in the minds of readers.This book is meant for data analysts, computer scientists/engineers, and industry professionals who are interested in creating different types of visualization graphs for a given data problem and drawing interesting insights from the plotted trends in order to make better business decisions in the future. Features: Introduces the world of Business Intelligence to readers through visualizations in Tableau. Discusses the need and relevance of each business graph with the help of a corresponding real-time case study. Explores the art of picking a suitable graph with an appropriate color scheme for a given scenario. Establishes the process of gaining relevant insights from the analysis of visualizations created. Provides guidance in creating innovative dashboards and driving the readers through the process of innovative storytelling with data in Tableau. Implements the concept of Exploratory Data Analysis (EDA) in Tableau.
Data Visualization for People of All Ages (ISSN)
by Nancy OrganData visualization is the art and science of making information visible. On paper and in our imaginations, it’s a language of shapes and colors that holds our best ideas and most important questions. As we find ourselves swimming in data of all kinds, visualization can help us to understand, express, and explore the richness of the world around us. No matter your age or background, this book opens the door to new ways of thinking and sharing through the power of data visualization.Data Visualization for People of All Ages is a field guide to visual literacy, born from the author’s personal experience working with world-class scholars, engineers, and scientists. By walking through the different ways of showing data—including color, angle, position, and length—you’ll learn how charts and graphs truly work so that no visualization is ever a mystery or out of reach. It doesn’t stop at what fits on a page, either. You’ll journey into cutting-edge topics like data sonification and data physicalization, using sound and touch to share data across the different senses. Packed with practical examples and exercises to help you connect the dots, this book will teach you how to create and understand data visualizations on your own—all without writing a single line of code or getting tangled up in software.Written with accessibility in mind, this book invites everyone to the table to share the joy of one of today’s most necessary skills. Perfect for home or classroom use, this friendly companion gives people of all ages everything they need to start visualizing with confidence.
Data Visualization in Excel: A Guide for Beginners, Intermediates, and Wonks (AK Peters Visualization Series)
by Jonathan SchwabishThis book closes the gap between what people think Excel can do and what they can achieve in the tool. Over the past few years, recognition of the importance of effectively visualizing data has led to an explosion of data analysis and visualization software tools. But for many people, Microsoft Excel continues to be the workhorse for their data visualization needs, not to mention the only tool that many data workers have access to. Although Excel is not a specialist data visualization platform, it does have strong capabilities. The default chart types do not need to be the limit of the tool’s data visualization capabilities, and users can extend its features by understanding some key elements and strategies. Data Visualization in Excel provides a step-by-step guide to creating more advanced and often more effective data visualizations in Excel and is the perfect guide for anyone who wants to create better, more effective, and more engaging data visualizations.