Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Toward Next-Generation Performance Budgeting: Lessons from the Experiences of Seven Reforming Countries analyzes the difficulties that national governments have had in linking measurement of performance and results to the annual budget process. The book is based on intensive reviews of four advanced countries that were early reformers and three pioneers in Central and Eastern Europe. In addition to looking at their current systems, Toward Next-Generation Performance Budgeting looks at how their approaches have evolved over time. This book attempts to fill a gap between survey-based self-assessments and best-practice guides. It was compiled in response to the concerns of budget departments in countries in Eastern Europe and Central Asia, many of which are committed to adopting some form of performance-based budgeting and are seeking to learn from the experiences of previous reformers what the practical challenges are and how they can adapt best-practice approaches to a messy reality. The case studies demonstrate a general pattern of disappointment with the results of performance budgeting, balanced by a strong belief in the underlying logic, which has resulted in repeated efforts to modify approaches to tighten the links between budgeting and performance. These efforts have resulted in significant variation in how countries have implemented performance budgeting and in the benefits they have derived. These variations offer guidance for models of next-generation performance budgeting, avoiding classic pitfalls, and incorporating modifications introduced by those who have used it longest and found it useful.

Program budgeting.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key Features Bridge your data analysis with the power of programming, complex algorithms, and AI Use Python and its extensive libraries to power your way to new levels of data insight Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series Explore this modern approach across with key industry case studies and hands-on projects Book Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you're likely to meet in today. The first of these is an image recognition application with TensorFlow ? embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learn A new toolset that has been carefully crafted to meet for your data analysis challenges Full and detailed case studies of the toolset across several of today's key industry contexts Become super productive with a new toolset across Python and Jupyter Notebook Look into the future of data science an...

Python (Computer program language)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Build Java Enterprise Applications and learn how Kotlin makes it easier to code them using components like JSF 2.3, Enterprise JavaBeans (EJB) 3.2, Contexts and Dependency Injection (CDI) 2.0, the Java API for WebSockets, JAX-RS 2.1, Servlet 4.0. Key Features An in-depth guide updated with all the latest features of Kotlin 1.2 and Java EE 8 Build microservices in Java EE with the help of Kotlin use cases Explore coroutines, garbage collection, multithreading, memory management and more Book Description Kotlin was developed with a view to solving programmers' difficulties and operational challenges. This book guides you in making Kotlin and Java EE work in unison to build enterprise-grade applications. Together, they can be used to create services of any size with just a few lines of code and let you focus on the business logic. Kotlin for Enterprise Applications using Java EE begins with a brief tour of Kotlin and helps you understand what makes it a popular and reasonable choice of programming language for application development, followed by its incorporation in the Java EE platform. We will then learn how to build applications using the Java Persistence API (JPA) and Enterprise JavaBeans (EJB), as well as develop RESTful web services and MicroServices. As we work our way through the chapters, we'll use various performance improvement and monitoring tools for your application and see how they optimize real-world applications. At each step along the way, we will see how easy it is to develop enterprise applications in Kotlin. By the end of this book, we will have learned design patterns and how to implement them using Kotlin. What you will learn Understand Kotlin syntax and appreciate why it's gaining in popularity Explore the Java EE ecosystem and the APIs in Java EE Implement applications using Kotlin Overcome the challenges of developing the Java EE system using Kotlin Gain insights into Java Message Services (JMS) Build RESTful MicroServices and secure applications Optimize applications with performance and monitoring tools Understand design patterns and implement them Who this book is for Kotlin for Enterprise Applications using Java EE is for Java EE developers who want to build their enterprise project or application with Kotlin or migrate from Java to Kotlin. Basic knowledge of programming is necessary to understand the key concepts covered in this book.

Java (Computer program language)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Architect scalable, reliable, and maintainable applications for enterprises with Python Key Features Explore various Python design patterns used for enterprise software development Apply best practices for testing and performance optimization to build stable applications Learn about different attacking strategies used on enterprise applications and how to avoid them Book Description Dynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you'll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You'll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python. What you will learn Understand the purpose of design patterns and their impact on application lifecycle Build applications that can handle large amounts of data-intensive operations Uncover advanced concurrency techniques and discover how to handle a large number of requests in production Optimize frontends to improve the client-side experience of your application Effective testing and performance profiling techniques to detect issues in applications early in the development cycle Build applications with a focus on security Implement large applications as microservices to improve scalability Who this book is for If you're a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to ...

Python (Computer program language)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R About This Book A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R Combine Tableau analytics and visualization with the power of R using this step-by-step guide Wondering how R can be used with Tableau? This book is your one-stop solution. Who This Book Is For This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau. A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book. What You Will Learn Integrate Tableau's analytics with the industry-standard, statistical prowess of R. Make R function calls in Tableau, and visualize R functions with Tableau using RServe. Use the CRISP-DM methodology to create a roadmap for analytics investigations. Implement various supervised and unsupervised learning algorithms in R to return values to Tableau. Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions. In Detail Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advan...

R (Computer program language)