Your answer to the software project management gap The Complete Software Project Manager: From Planning to Launch and Beyond addresses an interesting problem experienced by today's project managers: they are often leading software projects, but have no background in technology. To close this gap in experience and help you improve your software project management skills, this essential text covers key topics, including: how to understand software development and why it is so difficult, how to plan a project, choose technology platforms, and develop project specifications, how to staff a project, how to develop a budget, test software development progress, and troubleshoot problems, and what to do when it all goes wrong. Real-life examples, hints, and management tools help you apply these new ideas, and lists of red flags, danger signals, and things to avoid at all costs assist in keeping your project on track. Companies have, due to the nature of the competitive environment, been somewhat forced to adopt new technologies. Oftentimes, the professionals leading the development of these technologies do not have any experience in the tech field and this can cause problems. To improve efficiency and effectiveness, this groundbreaking book offers guidance to professionals who need a crash course in software project management. * Review the basics of software project management, and dig into the more complicated topics that guide you in developing an effective management approach * Avoid common pitfalls by perusing red flags, danger signals, and things to avoid at all costs * Leverage practical roadmaps, charts, and step-by-step processes * Explore real-world examples to see effective software project management in action The Complete Software Project Manager: From Planning to Launch and Beyond is a fundamental resource for professionals who are leading software projects but do not have a background in technology.

The purpose of this handbook is to allow users to learn and master the mathematics software package MATLAB (R), as well as to serve as a quick reference to some of the most used instructions in the package. A unique feature of this handbook is that it can be used by the novice and by experienced users alike. For experienced users, it has four chapters with examples and applications in engineering, finance, physics, and optimization. Exercises are included, along with solutions available for the interested reader on the book's web page. These exercises are a complement for the interested reader who wishes to get a deeper understanding of MATLAB. Features Covers both MATLAB and introduction to Simulink Covers the use of GUIs in MATLAB and Simulink Offers downloadable examples and programs from the handbook's website Provides an introduction to object oriented programming using MATLAB Includes applications from many areas Includes the realization of executable files for MATLAB programs and Simulink models

Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.

The Official Intuit Guide to QuickBooks 2013 for Windows

Your bookkeeping workflow will be smoother and faster with QuickBooks 2013 for Windows, and as the program's Official Guide, this Missing Manual puts you firmly in control. You get step-by-step instructions on how and when to use specific features, along with basic accounting advice to guide you through the learning process.

The important stuff you need to know: Get started. Set up your accounts, customers, jobs, and invoice items quickly.Follow the money. Track everything from billable time and expenses to income and profit.Keep your company financially fit. Examine budgets and actual spending, income, inventory, assets, and liabilities.Spend less time on bookkeeping. Use QuickBooks to create and reuse bills, invoices, sales receipts, and timesheets.Find key info fast. Rely on QuickBooks' Search and Find features, as well as the Vendor, Customer, Inventory, and Employee Centers.Exchange data with other programs. Move data between QuickBooks, Microsoft Office, and other programs.

This Element is intended for students and practitioners as a gentle and intuitive introduction to the field of discrete-time yield curve modelling. I strive to be as comprehensive as possible, while still adhering to the overall premise of putting a strong focus on practical applications. In addition to a thorough description of the Nelson-Siegel family of model, the Element contains a section on the intuitive relationship between P and Q measures, one on how the structure of a Nelson-Siegel model can be retained in the arbitrage-free framework, and a dedicated section that provides a detailed explanation for the Joslin, Singleton, and Zhu (2011) model.

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code. By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.

To write an accomplished program in the DATA step of SAS (R), programmers must understand programming logic and know how to implement and even create their own programming algorithm. Handbook of SAS (R) DATA Step Programming shows readers how best to manage and manipulate data by using the DATA step. The book helps novices avoid common mistakes resulting from a lack of understanding fundamental and unique SAS programming concepts. It explains that learning syntax does not solve all problems; rather, a thorough comprehension of SAS processing is needed for successful programming. The author also guides readers through a programming task. In most of the examples, the author first presents strategies and steps for solving the problem, then offers a solution, and finally gives a more detailed explanation of the solution. Understanding the DATA steps, particularly the program data vector (PDV), is critical to proper data manipulation and management in SAS. This book helps SAS programmers thoroughly grasp the concept of DATA step processing and write accurate programs in the DATA step. Numerous supporting materials, including data sets and programs used in the text, are available on the book's CRC Press web page.

While teaching the Numerical Methods for Engineers course over the last 15 years, the author found a need for a new textbook, one that was less elementary, provided applications and problems better suited for chemical engineers, and contained instruction in Visual Basic (R) for Applications (VBA). This led to six years of developing teaching notes that have been enhanced to create the current textbook, Numerical Methods for Chemical Engineers Using Excel (R), VBA, and MATLAB (R). Focusing on Excel gives the advantage of it being generally available, since it is present on every computer-PC and Mac-that has Microsoft Office installed. The VBA programming environment comes with Excel and greatly enhances the capabilities of Excel spreadsheets. While there is no perfect programming system, teaching this combination offers knowledge in a widely available program that is commonly used (Excel) as well as a popular academic software package (MATLAB). Chapters cover nonlinear equations, Visual Basic, linear algebra, ordinary differential equations, regression analysis, partial differential equations, and mathematical programming methods. Each chapter contains examples that show in detail how a particular numerical method or programming methodology can be implemented in Excel and/or VBA (or MATLAB in chapter 10). Most of the examples and problems presented in the text are related to chemical and biomolecular engineering and cover a broad range of application areas including thermodynamics, fluid flow, heat transfer, mass transfer, reaction kinetics, reactor design, process design, and process control. The chapters feature "Did You Know" boxes, used to remind readers of Excel features. They also contain end-of-chapter exercises, with solutions provided.

Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.

An authoritative introduction to the latest comparative methods in evolutionary biology Phylogenetic comparative methods are a suite of statistical approaches that enable biologists to analyze and better understand the evolutionary tree of life, and shed vital new light on patterns of divergence and common ancestry among all species on Earth. This textbook shows how to carry out phylogenetic comparative analyses in the R statistical computing environment. Liam Revell and Luke Harmon provide an incisive conceptual overview of each method along with worked examples using real data and challenge problems that encourage students to learn by doing. By working through this book, students will gain a solid foundation in these methods and develop the skills they need to interpret patterns in the tree of life. Covers every major method of modern phylogenetic comparative analysis in R Explains the basics of R and discusses topics such as trait evolution, diversification, trait-dependent diversification, biogeography, and visualization Features a wealth of exercises and challenge problems Serves as an invaluable resource for students and researchers, with applications in ecology, evolution, anthropology, disease transmission, conservation biology, and a host of other areas Written by two of today's leading developers of phylogenetic comparative methods

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them. The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

*When R creates copies, and how it affects memory usage and code performance *Everything you could ever want to know about functions *The differences between calling and exiting handlers *How to employ functional programming to solve modular tasks *The motivation, mechanics, usage, and limitations of R's highly pragmatic S3 OO system *The R6 OO system, which is more like OO programming in other languages *The rules that R uses to parse and evaluate expressions *How to use metaprogramming to generate HTML or LaTeX with elegant R code *How to identify and resolve performance bottlenecks

Operational Assessment of IT presents ideas and concepts of optimization designed to improve an organization's business processes and assist business units in meeting organizational goals more effectively. Rather than focus on specific technologies, computing environments, enterprise risks, resource programs, or infrastructure, the book focuses on organizational processes. Throughout the book, the author presents concerns and environments encountered throughout his career to demonstrate issues and explain how you, too, can successfully implement the tools presented in the book. The assessment process reviews the economics as well as the effectiveness and efficiency of the process. Whether your organization is profit-based, not-for-profit, or even governmental, you cannot provide services or products at a continuous loss. For an operational assessment to be of value, the ultimate goal must be to insure that the business unit process is effective and efficient and employs the financial assets and resources appropriately or helps the business unit make adjustments to improve the operation and use resources more efficiently and economically. After reading this book, you will be able to devise more efficient and economical ways to meet your customers' requirements, no matter who or where your customers are. You will learn that the goal of any process is to service or supply customers with what they want. The book provides tools and techniques that will assist you in gaining a 360-degree view of the process so that you can help the business unit improve the delivery of a quality product or a service to the customer.

In "Large-Scale Scrum," Craig Larman and Bas Vodde offer the most direct, concise, actionable guide to reaping the full benefits of agile in distributed, global enterprises. Larman and Vodde have distilled their immense experience helping geographically distributed development organizations move to agile. Going beyond their previous books, they offer today's fastest, most focused guidance: "brass tacks" advice and field-proven best practices for achieving value fast, and achieving even more value as you move forward. Targeted to enterprise project participants and stakeholders, "Large-Scale Scrum" offers straight-to-the-point insights for scaling Scrum across the entire project lifecycle, from sprint planning to retrospective. Larman and Vodde help you:

• Implement proven Scrum frameworks for large-scale developments
• Scale requirements, planning, and product management
• Scale design and architecture
• Effectively manage defects and interruptions
• Integrate Scrum into multisite and offshore projects
• Choose the right adoption strategies and organizational designs

The book deals with functions of many variables: differentiation and integration, extrema with a number of digressions to related subjects such as curves, surfaces and Morse theory. The background needed for understanding the examples and how to compute in Mathematica (R) will also be discussed.

This textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software. The authors provide a concise, practical guide that takes students from the early stages of research design, with a jargon-free explanation of terminology, and walks them through key analyses such as the t-test, ANOVA, correlation, chi-square, and linear regression. The book features written interpretations to help learners identify relevant statistics along the way. With fascinating examples from psychological research, as well as screenshots and activities from jamovi, this text is sure to encourage even the most reluctant statistics student. The comprehensive companion website provides an extra helping hand, with practice datasets and a full suite of tutorial videos to help consolidate understanding. This is essential reading for psychology students using jamovi for their courses in Research Methods and Statistics or Data Analysis.

This book constitutes the refereed proceedings of the 15th Conference on Computability in Europe, CiE 2019, held in Durham, UK, in July 2019. The 20 revised full papers presented were carefully reviewed and selected from 35 submissions. In addition, this volume includes 7 invited papers. The conference CiE 2018 had the following six special sessions: computational neuroscience, history and philosophy of computing, lowness notions in computability, probabilistic programming and higher-order computation, smoothed and probabilistic analysis of algorithms, and transnite computations.

Designed for engineers, computer scientists, and physicists or for use as a textbook in computational courses, Applied Linear Algebra & Optimization Using MATLAB, provides the reader with numerous applications, m-files, and practical examples to solve problems. Balancing theoretical concepts with computational speed and accuracy, the book includes numerous short programs in MATLAB that can be used to solve problems involving systems of linear equations, matrices, vectors, computer graphics, and more. The book is accompanied by a CD-ROM with all of the figures, m-files for all of the programs, and MATLAB simulations from industry. Complete solutions and Microsoft PowerPoint slides are available to instructors for use as a textbook.Brief Table of Contents: 1. Matrices and Linear Systems. 2. Iterative Methods for Linear Systems. 3. The Eigenvalue Problems. 4. Numerical Computation of Eigenvalues. 5. Interpolation and Approximation. 6. Linear Programming. 7. Nonlinear Programming. Appendices. About the CD-ROM

Complex Survey Data Analysis with SAS (R) is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT (R) procedures. The book offers comprehensive coverage of the most essential topics, including: Drawing random samples Descriptive statistics for continuous and categorical variables Fitting and interpreting linear and logistic regression models Survival analysis Domain estimation Replication variance estimation methods Weight adjustment and imputation methods for handling missing data The easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author's website: http://mason.gmu.edu/~tlewis18/. While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation. Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.

This book contains the refereed proceedings of the 14th International Symposium on Mathematical Morphology, ISMM 2019, held in Saarbrucken, Germany, in July 2019. The 40 revised full papers presented together with one invited talk were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on Theory, Discrete Topology and Tomography, Trees and Hierarchies, Multivariate Morphology, Computational Morphology, Machine Learning, Segmentation, Applications in Engineering, and Applications in (Bio)medical Imaging.

The Professional Product Owner's Guide to Maximizing Value with Scrum "This book presents a method of communicating our desires, cogently, coherently, and with a minimum of fuss and bother." -Ken Schwaber, Chairman & Founder, Scrum.org The role of the Product Owner is more crucial than ever. But it's about much more than mechanics: it's about taking accountability and refocusing on value as the primary objective of all you do. In The Professional Product Owner, two leading experts in successful Scrum product ownership show exactly how to do this. You'll learn how to identify where value can be found, measure it, and maximize it throughout your entire product lifecycle. Drawing on their combined 40+ years of experience in using agile and Scrum in product management, Don McGreal and Ralph Jocham guide you through all facets of envisioning, emerging, and maturing a product using the Scrum framework. McGreal and Jocham discuss strategy, showing how to connect Vision, Value, and Validation in ROI-focused agile product management. They lay out Scrum best-practices for managing complexity and continuously delivering value, and they define the concrete practices and tools you can use to manage Product Backlogs and release plans, all with the goal of making you a more successful Product Owner. Throughout, the authors share revealing personal experiences that illuminate obstacles to success and show how they can be overcome. Define success from the "outside in," using external customer-driven measurements to guide development and maximize value Bring empowerment and entrepreneurship to the Product Owner's role, and align everyone behind a shared business model Use Evidence-Based Management (EBMgt) to invest in the right places, make smarter decisions, and reduce risk Effectively apply Scrum's Product Owner role, artifacts, and events Populate and manage Product Backlogs, and use just-in-time specifications Plan and manage releases, improve transparency, and reduce technical debt Scale your product, not your Scrum Use Scrum to inject autonomy, mastery, and purpose into your product team's work Whatever your role in product management or agile development, this guide will help you deliver products that offer more value, more rapidly, and more often. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications. It focuses on statistical estimation and selection methods for vine copulas in data applications. These flexible copula models can successfully accommodate any form of tail dependence and are vital to many applications in finance, insurance, hydrology, marketing, engineering, chemistry, aviation, climatology and health. The book explains the pair-copula construction principles underlying these statistical models and discusses how to perform model selection and inference. It also derives simulation algorithms and presents real-world examples to illustrate the methodological concepts. The book includes numerous exercises that facilitate and deepen readers' understanding, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch. In closing, the book provides insights into recent developments and open research questions in vine copula based modeling. The book is intended for students as well as statisticians, data analysts and any other quantitatively oriented researchers who are new to the field of vine copulas. Accordingly, it provides the necessary background in multivariate statistics and copula theory for exploratory data tools, so that readers only need a basic grasp of statistics and probability.

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Multiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python. Existing methods are presented in detail with a step by step learning approach. Theoretical background is given for TOPSIS, VIKOR, PROMETHEE, SIR, AHP, goal programming, and their variations. Comprehensive numerical examples are also discussed for each method in conjunction with easy to follow Python code. Extensions to multiple criteria decision making algorithms such as fuzzy number theory and group decision making are introduced and implemented through Python as well. Readers will learn how to implement and use each method based on the problem, the available data, the stakeholders involved, and the various requirements needed. Focusing on the practical aspects of the multiple criteria decision making methodologies, this book is designed for researchers, practitioners and advanced graduate students in the applied mathematics, information systems, operations research and business administration disciplines, as well as other engineers and scientists oriented in interdisciplinary research. Readers will greatly benefit from this book by learning and applying various MCDM/A methods. (Adiel Teixeira de Almeida, CDSID-Center for Decision System and Information Development, Universidade Federal de Pernambuco, Recife, Brazil) Promoting the development and application of multicriteria decision aid is essential to ensure more ethical and sustainable decisions. This book is a great contribution to this objective. It is a perfect blend of theory and practice, providing potential users and researchers with the theoretical bases of some of the best-known methods as well as with the computing tools needed to practice, to compare and to put these methods to use. (Jean-Pierre Brans, Vrije Universiteit Brussel, Brussels, Belgium) This book is intended for researchers, practitioners and students alike in decision support who wish to familiarize themselves quickly and efficiently with multicriteria decision aiding algorithms. The proposed approach is original, as it presents a selection of methods from the theory to the practical implementation in Python, including a detailed example. This will certainly facilitate the learning of these techniques, and contribute to their effective dissemination in applications. (Patrick Meyer, IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, Brest, France)