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Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key Features Learn how to wrangle different types of datasets and analytics problems into networks Leverage graph theoretic algorithms to analyze data efficiently Apply the skills you gain to solve a variety of problems through case studies in Python Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learn Transform different data types, such as spatial data, into network formats Explore common network science tools in Python Discover how geometry impacts spreading processes on networks Implement machine learning algorithms on network data features Build and query graph databases Explore new frontiers in network science such as quantum algorithms Who this book is for If you’re a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
This book guides the reader in the analysis of big-data by providing theoretical and practical instruments to tame the complexity of such systems. Together with support provided by the companion website, it constitutes a simple and useful handbook for data analysts.
We construct a new, comprehensive instrument-level database of sovereign debt for 18 advanced and emerging countries over the period 1913–46. The database contains data on amounts outstanding for some 3,800 individual debt instruments as well as associated qualitative information, including instrument type, coupon rate, maturity, and currency of issue. This information can provide unique insights into various policies implemented in the interwar period, which was characterized by notoriously high debt levels. We document how interwar governments rolled over debts that were largely unsustainable and how the external public debt network contributed to the collapse of the international financial system in the early 1930s.
VB Programmers: Get in Step with .NET With the introduction of Visual Basic .NET, VB transcends its traditional second-class status to become a full-fledged citizen of the object-oriented programming, letting you access the full power of the Windows platform for the first time. Written bythe author of the best-selling Mastering Visual Basic 6 this all-new edition is the resource you need to make a successful transition to .NET. Comprising in-depth explanations, practical examples, and handy reference information, its coverage includes: Mastering the new Windows Forms Designer and controls Building dynamic forms Using powerful Framework classes such as ArrayLists and HashTables Persisting objects to disk files Handling graphics and printing Achieving robustness via structured exception handling and debugging Developing your own classes and extending existing ones via inheritance Building custom Windows controls Building menus and list controls with custom-drawn items Using ADO.NET to build disconnected, distributed applications Using SQL queries and stored procedures with ADO.NET Facilitating database programming with the visual database tools Building web applications with ASP.NET and the rich web controls Designing web applications to access databases Using the DataGrid and DataList web controls Building XML web services to use with Windows and web applications Special topics like the Multiple Document Interface and powerful recursive programming techniques Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Exporting Communication Technology to Developing Countries analyzes the economic, cultural, educational, and political implications of exporting information technology to Africa. Emmanuel K. Ngwainmbi tackles the numerous complexities related to the development of African telecommunications. He not only presents the technological aspects of telecommunications, but effectively depicts the cultural implications that must be considered. Ngwainmbi investigates and evaluates the history of African telecommunications, clearly showing the path that has led to its present state. He then assesses the potential for development within the countries of Africa upon the influx of technology from the outside world.
Offering a unique approach to learning XML, this book walks readers through the process of building a complete, functional, end-to-end XML solution Featured case study is an online business product catalog that includes reports, data input/output, workflow, stylesheet formatting, RSS feeds, and integration with external services like Google, eBay, and Amazon The format of presenting a problem and working through the design to come up with a solution enables readers to understand how XML markup allows a business to share data across applications internally or with partners or customers even though they might not use the same applications
This book gives readers the theoretical and empirical methods to analyze applied economics. They are institutional economics, information economics, environmental economics, international economics, financial economics, industrial organization, public economics, law and economics, and spatial economics. Because the chapters of this book deal with current topics in these categories, they are relevant not only to researchers and graduate students but also to policy makers and entrepreneurs. As there is uncertainty about the global economy, it is necessary to consider optimal, efficient behavior to survive in the confused world. The book is organized in three parts. Part 1 deals with institutional economics, information economics, and related topics, approached through game theory. Part 2 focuses on environmental economics, international economics, and financial economics, through a microeconomic or econometric approach. Finally, Part 3 concentrates on public economics, social security, and related fields, through microeconomics or macroeconomics.