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1. |
Record Nr. |
UNINA9910682589503321 |
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Autore |
Bass Julian Michael |
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Titolo |
Agile Software Engineering Skills / / by Julian Michael Bass |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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9783031054693 |
9783031054686 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (328 pages) |
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Disciplina |
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Soggetti |
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Software engineering |
Business information services |
Computers |
Professions |
Software Engineering |
IT in Business |
The Computing Profession |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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This textbook is about working in teams to create functioning software. It covers skills in agile software development methods, team working, version control and continuous integration and shows readers how to apply some of the latest ideas from lean, agile and Kanban. Part I, which focuses on People, describes various project roles and the skills needed to perform each role. This includes members of self-organizing teams, scrum masters, product owners and activities for managing other stakeholders. The skills needed to create Product artefacts are detailed in Part II. These include skills to create agile requirements, architectures, designs as well as development and security artefacts. The agile development Process to coordinate with co-workers is described in Part III. It introduces the skills needed to facilitate an incremental process and to use software tools for version control and |
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automated testing. Eventually some moreadvanced topics are explained in Part IV. These topics include large projects comprising multiple cooperating teams, automating deployment, cloud software services, DevOps and evolving live systems. This textbook addresses significant competencies in the IEEE/ACM Computing Curricula Task Force 2020. It includes nearly 100 exercises for trying out and applying the skills needed for agile software development. Hints, tips and further advice about tackling the exercises are presented at the end of each chapter, and a case study project, with downloadable source code from an online repository, integrates the skills learned across the chapters. In addition, further example software projects are also available there. This way, the book provides a hands-on guide to working on a development project as part of a team, and is inspired by the needs of early career practitioners as well as undergraduate software engineering and computer science students. |
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2. |
Record Nr. |
UNINA9910155108403321 |
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Autore |
Mallinckrodt Craig H. <1958, > |
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Titolo |
Analyzing longitudinal clinical trial data : a practical guide / / by Craig Mallinckrodt and Ilya Lipkovich |
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Pubbl/distr/stampa |
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Boca Raton, FL : , : Chapman and Hall/CRC, an imprint of Taylor and Francis, , 2016 |
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ISBN |
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1-351-73768-6 |
1-315-18663-2 |
1-351-73769-4 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (330 pages) : illustrations, tables |
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Collana |
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Chapman & Hall/CRC Biostatistics Series |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- List of Tables -- List of Figures -- List of Code Fragments -- Section I: Background and Setting -- 1: Introduction -- 2: Objectives and Estimands�Determining What to |
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Estimate -- 3: Study Design�Collecting the Intended Data -- 4: Example Data -- 5: Mixed-Effects Models Review -- Section II: Modeling the Observed Data -- 6: Choice of Dependent Variable and Statistical Test -- 7: Modeling Covariance (Correlation) -- 8: Modeling Means Over Time -- 9: Accounting for Covariates -- 10: Categorical Data -- 11: Model Checking and Verification -- Section III: Methods for Dealing with Missing Data -- 12: Overview of Missing Data -- 13: Simple and Ad Hoc Approaches for Dealing with Missing Data -- 14: Direct Maximum Likelihood -- 15: Multiple Imputation -- 16: Inverse Probability Weighted Generalized Estimated Equations -- 17: Doubly Robust Methods -- 18: MNAR Methods -- 19: Methods for Incomplete Categorical Data -- Section IV: A Comprehensive Approach to Study Development and Analyses -- 20: Developing Statistical Analysis Plans -- 21: Example Analyses of Clinical Trial Data -- References -- Index. |
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Sommario/riassunto |
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Analyzing Longitudinal Clinical Trial Data: A Practical Guide provides practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice. The book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan. |
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