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| agile data science download: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
| agile data science download: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| agile data science download: Agile Data Science Russell Jurney, 2013-10-15 Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track |
| agile data science download: Practical DataOps Harvinder Atwal, 2019-12-10 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will Learn Develop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| agile data science download: Agile Data Warehousing Project Management Ralph Hughes, 2012-09-28 What is agile data warehousing? -- Iterative development in a nutshell -- Streamlining project management -- Authoring better user stories -- Deriving initial project backlogs -- Developer stories for data integration -- Estimating and segmenting projects -- Adapting agile for data warehousing -- Starting and scaling agile data warehousing. |
| agile data science download: Agile Artificial Intelligence in Pharo Alexandre Bergel, 2020-06-20 Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains. Along the way, you’ll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You’ll be able to take this source code and apply it to your own projects. What You Will Learn Use neurons, neural networks, learning theory, and more Work with genetic algorithms Incorporate neural network principles when working towards neuroevolution Include neural network fundamentals when building three Pharo-based applications Who This Book Is For Coders and data scientists who are experienced programmers and have at least some prior experience with AI or deep learning. They may be new to Pharo programming, but some prior experience with it would be helpful. |
| agile data science download: Agile Data Warehousing for the Enterprise Ralph Hughes, 2015-09-19 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. - Learn how to quickly define scope and architecture before programming starts - Includes techniques of process and data engineering that enable iterative and incremental delivery - Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing - Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges - Use the provided 120-day road map to establish a robust, agile data warehousing program |
| agile data science download: Data Analytics in Project Management Seweryn Spalek, J. Davidson Frame, Yanping Chen, Carl Pritchard, Alfonso Bucero, Werner Meyer, Ryan Legard, Michael Bragen, Klas Skogmar, Deanne Larson, Bert Brijs, 2019-01-01 Data Analytics in Project Management. Data analytics plays a crucial role in business analytics. Without a rigid approach to analyzing data, there is no way to glean insights from it. Business analytics ensures the expected value of change while that change is implemented by projects in the business environment. Due to the significant increase in the number of projects and the amount of data associated with them, it is crucial to understand the areas in which data analytics can be applied in project management. This book addresses data analytics in relation to key areas, approaches, and methods in project management. It examines: • Risk management • The role of the project management office (PMO) • Planning and resource management • Project portfolio management • Earned value method (EVM) • Big Data • Software support • Data mining • Decision-making • Agile project management Data analytics in project management is of increasing importance and extremely challenging. There is rapid multiplication of data volumes, and, at the same time, the structure of the data is more complex. Digging through exabytes and zettabytes of data is a technological challenge in and of itself. How project management creates value through data analytics is crucial. Data Analytics in Project Management addresses the most common issues of applying data analytics in project management. The book supports theory with numerous examples and case studies and is a resource for academics and practitioners alike. It is a thought-provoking examination of data analytics applications that is valuable for projects today and those in the future. |
| agile data science download: Agile Machine Learning with DataRobot Bipin Chadha, Sylvester Juwe, 2021-12-24 Leverage DataRobot's enterprise AI platform and automated decision intelligence to extract business value from data Key FeaturesGet well-versed with DataRobot features using real-world examplesUse this all-in-one platform to build, monitor, and deploy ML models for handling the entire production life cycleMake use of advanced DataRobot capabilities to programmatically build and deploy a large number of ML modelsBook Description DataRobot enables data science teams to become more efficient and productive. This book helps you to address machine learning (ML) challenges with DataRobot's enterprise platform, enabling you to extract business value from data and rapidly create commercial impact for your organization. You'll begin by learning how to use DataRobot's features to perform data prep and cleansing tasks automatically. The book then covers best practices for building and deploying ML models, along with challenges faced while scaling them to handle complex business problems. Moving on, you'll perform exploratory data analysis (EDA) tasks to prepare your data to build ML models and ways to interpret results. You'll also discover how to analyze the model's predictions and turn them into actionable insights for business users. Next, you'll create model documentation for internal as well as compliance purposes and learn how the model gets deployed as an API. In addition, you'll find out how to operationalize and monitor the model's performance. Finally, you'll work with examples on time series forecasting, NLP, image processing, MLOps, and more using advanced DataRobot capabilities. By the end of this book, you'll have learned to use DataRobot's AutoML and MLOps features to scale ML model building by avoiding repetitive tasks and common errors. What you will learnUnderstand and solve business problems using DataRobotUse DataRobot to prepare your data and perform various data analysis tasks to start building modelsDevelop robust ML models and assess their results correctly before deploymentExplore various DataRobot functions and outputs to help you understand the models and select the one that best solves the business problemAnalyze a model's predictions and turn them into actionable insights for business usersUnderstand how DataRobot helps in governing, deploying, and maintaining ML modelsWho this book is for This book is for data scientists, data analysts, and data enthusiasts looking for a practical guide to building and deploying robust machine learning models using DataRobot. Experienced data scientists will also find this book helpful for rapidly exploring, building, and deploying a broader range of models. The book assumes a basic understanding of machine learning. |
| agile data science download: Agile Data Warehouse Design Lawrence Corr, Jim Stagnitto, 2011-11 Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling + brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset! Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun! ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino. |
| agile data science download: Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products Mora, Manuel, Gómez, Jorge Marx, O'Connor, Rory V., Buchalcevová, Alena, 2020-07-10 The highly dynamic world of information technology service management stresses the benefits of the quick and correct implementation of IT services. A disciplined approach relies on a separate set of assumptions and principles as an agile approach, both of which have complicated implementation processes as well as copious benefits. Combining these two approaches to enhance the effectiveness of each, while difficult, can yield exceptional dividends. Balancing Agile and Disciplined Engineering and Management Approaches for IT Services and Software Products is an essential publication that focuses on clarifying theoretical foundations of balanced design methods with conceptual frameworks and empirical cases. Highlighting a broad range of topics including business trends, IT service, and software development, this book is ideally designed for software engineers, software developers, programmers, information technology professionals, researchers, academicians, and students. |
| agile data science download: Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution Bolat, Hür Bersam, Temur, Gül Tekin, 2019-03-15 Communication between man and machine is vital to completing projects in the current day and age. Without this constant connectiveness as we enter an era of big data, project completion will result in utter failure. Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution addresses changes wrought by Industry 4.0 and its effects on project management as well as adaptations and adjustments that will need to be made within project life cycles and project risk management. Highlighting such topics as agile planning, cloud projects, and organization structure, it is designed for project managers, executive management, students, and academicians. |
| agile data science download: Research Anthology on Agile Software, Software Development, and Testing Information Resources Management Association, 2022 Software development continues to be an ever-evolving field as organizations require new and innovative programs that can be implemented to make processes more efficient, productive, and cost-effective. Agile practices particularly have shown great benefits for improving the effectiveness of software development and its maintenance due to their ability to adapt to change. It is integral to remain up to date with the most emerging tactics and techniques involved in the development of new and innovative software. The Research Anthology on Agile Software, Software Development, and Testing is a comprehensive resource on the emerging trends of software development and testing. This text discusses the newest developments in agile software and its usage spanning multiple industries. Featuring a collection of insights from diverse authors, this research anthology offers international perspectives on agile software. Covering topics such as global software engineering, knowledge management, and product development, this comprehensive resource is valuable to software developers, software engineers, computer engineers, IT directors, students, managers, faculty, researchers, and academicians. |
| agile data science download: Intelligence-Based Medicine Anthony C. Chang, 2020-06-24 Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced.The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies.The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. |
| agile data science download: The Art of Agile Development James Shore, Shane Warden, 2008 For those considering Extreme Programming, this book provides no-nonsense advice on agile planning, development, delivery, and management taken from the authors' many years of experience. While plenty of books address the what and why of agile development, very few offer the information users can apply directly. |
| agile data science download: Agile Project Management: Managing for Success James A. Crowder, Shelli Friess, 2014-08-23 Management and enables them to deal with the demands and complexities of modern, agile systems/software/hardware development teams. The book examines the project/program manager beyond the concepts of leadership and aims to connect to employees' sense of identity. The text examines human psychological concepts such as “locus of control,” which will help the manager understand their team members’ view and how best to manage their “world” contributions. The authors cover new management tools and philosophies for agile systems/software/hardware development teams, with a specific focus on how this relates to engineering and computer science. This book also includes practical case studies. Discusses management skills needed as they relate to the advances in software development practices Examines how to manage an agile development team that includes teams across geographically, ethnically, and culturally diverse backgrounds Embraces all of the aspects of modern management and leadership |
| agile data science download: Agile Software Development Thomas Stober, Uwe Hansmann, 2010-04-29 Software Development is moving towards a more agile and more flexible approach. It turns out that the traditional waterfall model is not supportive in an environment where technical, financial and strategic constraints are changing almost every day. But what is agility? What are today’s major approaches? And especially: What is the impact of agile development principles on the development teams, on project management and on software architects? How can large enterprises become more agile and improve their business processes, which have been existing since many, many years? What are the limitations of Agility? And what is the right balance between reliable structures and flexibility? This book will give answers to these questions. A strong emphasis will be on real life project examples, which describe how development teams have moved from a waterfall model towards an Agile Software Development approach. |
| agile data science download: Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications Rahman El Sheikh, Asim Abdel, Alnoukari, Mouhib, 2011-09-30 Business intelligence applications are of vital importance as they help organizations manage, develop, and communicate intangible assets such as information and knowledge. Organizations that have undertaken business intelligence initiatives have benefited from increases in revenue, as well as significant cost savings.Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications highlights the marriage between business intelligence and knowledge management through the use of agile methodologies. Through its fifteen chapters, this book offers perspectives on the integration between process modeling, agile methodologies, business intelligence, knowledge management, and strategic management. |
| agile data science download: Agile Processes in Software Engineering and Extreme Programming Peggy Gregory, Casper Lassenius, Xiaofeng Wang, Philippe Kruchten, 2021-06-09 This open access book constitutes the proceedings of the 22nd International Conference on Agile Software Development, XP 2021, which was held virtually during June 14-18, 2021. XP is the premier agile software development conference combining research and practice. It is a unique forum where agile researchers, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. XP conferences provide an informal environment to learn and trigger discussions and welcome both people new to agile and seasoned agile practitioners. This year’s conference was held with the theme “Agile Turns Twenty While the World Goes Online”. The 11 full and 2 short papers presented in this volume were carefully reviewed and selected from 38 submissions. They were organized in topical sections named: agile practices; process assessment; large-scale agile; and short contributions. |
| agile data science download: Data Science at the Command Line Jeroen Janssens, 2014-09-25 This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| agile data science download: Agile Software Development Torgeir Dingsøyr, Tore Dybå, Nils Brede Moe, 2014-10-14 Agile software development has become an umbrella term for a number of changes in how software developers plan and coordinate their work, how they communicate with customers and external stakeholders, and how software development is organized in small, medium, and large companies, from the telecom and healthcare sectors to games and interactive media. Still, after a decade of research, agile software development is the source of continued debate due to its multifaceted nature and insufficient synthesis of research results. Dingsøyr, Dybå, and Moe now present a comprehensive snapshot of the knowledge gained over many years of research by those working closely with or in the industry. It shows the current state of research on agile software development through an introduction and ten invited contributions on the main research fields, each written by renowned experts. These chapters cover three main issues: foundations and background of agile development, agile methods in practice, and principal challenges and new frontiers. They show the important results in each subfield, and in addition they explain what these results mean to practitioners as well as for future research in the field. The book is aimed at reflective practitioners and researchers alike, and it also can serve as the basis for graduate courses at universities. |
| agile data science download: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-21 A practical field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. |
| agile data science download: Clean Code Robert C. Martin, 2008-08-01 Even bad code can function. But if code isn’t clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship. Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code “on the fly” into a book that will instill within you the values of a software craftsman and make you a better programmer–but only if you work at it. What kind of work will you be doing? You’ll be reading code–lots of code. And you will be challenged to think about what’s right about that code, and what’s wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code–of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding How to tell the difference between good and bad code How to write good code and how to transform bad code into good code How to create good names, good functions, good objects, and good classes How to format code for maximum readability How to implement complete error handling without obscuring code logic How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code. |
| agile data science download: Data Science and Big Data Analytics EMC Education Services, 2015-01-27 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| agile data science download: Agile Risk Management Alan Moran, 2014-03-18 This work is the definitive guide for IT managers and agile practitioners. It elucidates the principles of agile risk management and how these relate to individual projects. Explained in clear and concise terms, this synthesis of project risk management and agile techniques is illustrated using the major methodologies such as XP, Scrum and DSDM. Although the agile community frequently cites risk management, research suggests that risk is often narrowly defined and, at best, implicitly treated, which in turn leads to an inability to make informed decisions concerning risk and reward and a poor understanding of when to engage in risk-related activities. Moreover, the absence of reference to enterprise risk management means that project managers are unable to clearly articulate scope or tailor their projects in line with the wider expectations of the organisation. Yet the agile approach, with its rich toolset of techniques, is very well equipped to effectively and efficiently deal with the risks that arise in projects. Alan Moran addresses the above issues by proposing an agile risk-management process derived from classical risk management but adapted to the circumstances of agile projects. Though his main focus is on the software development process, much of what he describes could be applied to other types of IT projects as well. This book is intended for anyone who is serious about balancing risk and reward in the pursuit of value for their stakeholders, and in particular for those directly involved in agile software development who share a concern for how risk should be managed. Whilst a thorough background in risk management is not presumed, a basic level of familiarity with or exposure to agility is helpful. |
| agile data science download: Agile Software Architecture Muhammad Ali Babar, Alan W. Brown, Ivan Mistrik, 2013-11-27 Agile software development approaches have had significant impact on industrial software development practices. Today, agile software development has penetrated to most IT companies across the globe, with an intention to increase quality, productivity, and profitability. Comprehensive knowledge is needed to understand the architectural challenges involved in adopting and using agile approaches and industrial practices to deal with the development of large, architecturally challenging systems in an agile way. Agile Software Architecture focuses on gaps in the requirements of applying architecture-centric approaches and principles of agile software development and demystifies the agile architecture paradox. Readers will learn how agile and architectural cultures can co-exist and support each other according to the context. Moreover, this book will also provide useful leads for future research in architecture and agile to bridge such gaps by developing appropriate approaches that incorporate architecturally sound practices in agile methods. - Presents a consolidated view of the state-of-art and state-of-practice as well as the newest research findings - Identifies gaps in the requirements of applying architecture-centric approaches and principles of agile software development and demystifies the agile architecture paradox - Explains whether or not and how agile and architectural cultures can co-exist and support each other depending upon the context - Provides useful leads for future research in both architecture and agile to bridge such gaps by developing appropriate approaches, which incorporate architecturally sound practices in agile methods |
| agile data science download: Agile Software Requirements Dean Leffingwell, 2010-12-27 “We need better approaches to understanding and managing software requirements, and Dean provides them in this book. He draws ideas from three very useful intellectual pools: classical management practices, Agile methods, and lean product development. By combining the strengths of these three approaches, he has produced something that works better than any one in isolation.” –From the Foreword by Don Reinertsen, President of Reinertsen & Associates; author of Managing the Design Factory; and leading expert on rapid product development Effective requirements discovery and analysis is a critical best practice for serious application development. Until now, however, requirements and Agile methods have rarely coexisted peacefully. For many enterprises considering Agile approaches, the absence of effective and scalable Agile requirements processes has been a showstopper for Agile adoption. In Agile Software Requirements, Dean Leffingwell shows exactly how to create effective requirements in Agile environments. Part I presents the “big picture” of Agile requirements in the enterprise, and describes an overall process model for Agile requirements at the project team, program, and portfolio levels Part II describes a simple and lightweight, yet comprehensive model that Agile project teams can use to manage requirements Part III shows how to develop Agile requirements for complex systems that require the cooperation of multiple teams Part IV guides enterprises in developing Agile requirements for ever-larger “systems of systems,” application suites, and product portfolios This book will help you leverage the benefits of Agile without sacrificing the value of effective requirements discovery and analysis. You’ll find proven solutions you can apply right now–whether you’re a software developer or tester, executive, project/program manager, architect, or team leader. |
| agile data science download: Visual Analytics for Data Scientists Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel, 2020-08-30 This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows. |
| agile data science download: Clean Agile Robert Martin, Robert C. Martin, 2019-10-14 Shows how to bring unprecedented levels of professionalism and discipline to agile development - and thereby write far more effective, successful software |
| agile data science download: Malware Data Science Joshua Saxe, Hillary Sanders, 2018-09-25 Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a big data problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve. |
| agile data science download: Learning Agile Andrew Stellman, Jennifer Greene, 2014-11-12 Learning Agile is a comprehensive guide to the most popular agile methods, written in a light and engaging style that makes it easy for you to learn. Agile has revolutionized the way teams approach software development, but with dozens of agile methodologies to choose from, the decision to go agile can be tricky. This practical book helps you sort it out, first by grounding you in agile’s underlying principles, then by describing four specific—and well-used—agile methods: Scrum, extreme programming (XP), Lean, and Kanban. Each method focuses on a different area of development, but they all aim to change your team’s mindset—from individuals who simply follow a plan to a cohesive group that makes decisions together. Whether you’re considering agile for the first time, or trying it again, you’ll learn how to choose a method that best fits your team and your company. Understand the purpose behind agile’s core values and principles Learn Scrum’s emphasis on project management, self-organization, and collective commitment Focus on software design and architecture with XP practices such as test-first and pair programming Use Lean thinking to empower your team, eliminate waste, and deliver software fast Learn how Kanban’s practices help you deliver great software by managing flow Adopt agile practices and principles with an agile coach |
| agile data science download: Agile 2 Cliff Berg, Kurt Cagle, Lisa Cooney, Philippa Fewell, Adrian Lander, Raj Nagappan, Murray Robinson, 2021-03-09 Agile is broken. Most Agile transformations struggle. According to an Allied Market Research study, 63% of respondents stated the failure of agile implementation in their organizations. The problems with Agile start at the top of most organizations with executive leadership not getting what agile is or even knowing the difference between success and failure in agile. Agile transformation is a journey, and most of that journey consists of people learning and trying new approaches in their own work. An agile organization can make use of coaches and training to improve their chances of success. But even then, failure remains because many Agile ideas are oversimplifications or interpreted in an extreme way, and many elements essential for success are missing. Coupled with other ideas that have been dogmatically forced on teams, such as agile team rooms, and an overall inertia and resistance to change in the Agile community, the Agile movement is ripe for change since its birth twenty years ago. Agile 2 represents the work of fifteen experienced Agile experts, distilled into Agile 2: The Next Iteration of Agile by seven members of the team. Agile 2 values these pairs of attributes when properly balanced: thoughtfulness and prescription; outcomes and outputs, individuals and teams; business and technical understanding; individual empowerment and good leadership; adaptability and planning. With a new set of Agile principles to take Agile forward over the next 20 years, Agile 2 is applicable beyond software and hardware to all parts of an agile organization including Agile HR, Agile Finance, and so on. Like the original Agile, Agile 2, is just a set of ideas - powerful ideas. To undertake any endeavor, a single set of ideas is not enough. But a single set of ideas can be a powerful guide. |
| agile data science download: Agile Processes in Software Engineering and Extreme Programming Viktoria Stray, Rashina Hoda, Maria Paasivaara, Philippe Kruchten, 2020-05-27 This open access book constitutes the proceedings of the 21st International Conference on Agile Software Development, XP 2020, which was planned to be held during June 8-12, 2020, at the IT University of Copenhagen, Denmark. However, due to the COVID-19 pandemic the conference was postponed until an undetermined date. XP is the premier agile software development conference combining research and practice. It is a hybrid forum where agile researchers, academics, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. Following this history, for both researchers and seasoned practitioners XP 2020 provided an informal environment to network, share, and discover trends in Agile for the next 20 years. The 14 full and 2 short papers presented in this volume were carefully reviewed and selected from 37 submissions. They were organized in topical sections named: agile adoption; agile practices; large-scale agile; the business of agile; and agile and testing. |
| agile data science download: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| agile data science download: Data Analytics with Hadoop Benjamin Bengfort, Jenny Kim, 2016-06 Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib |
| agile data science download: New Horizons in Design Science: Broadening the Research Agenda Brian Donnellan, Markus Helfert, Jim Kenneally, Debra VanderMeer, Marcus Rothenberger, Robert Winter, 2015 This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2015, held in Dublin, Ireland, in May 2015. The 22 full papers, 11 short papers and 10 short papers describing prototypes and products were carefully reviewed and selected from 111 submissions. The papers are organized in topical sections on design science research in action; meta perspectives; data mining and analytics; emerging themes; design practice and design thinking; and prototypes. |
| agile data science download: Innovative and Agile Contracting for Digital Transformation and Industry 4.0 Mohammad Ali Shalan, Mohammed Ayedh Algarni, 2021 This book introduces innovative contracting to fit the era of digital transformation and industry 4.0 by addressing the motivations to promote such a new style of contracting and listing the shortcomings of traditional contracting-- |
| agile data science download: Data Science at the Command Line Jeroen Janssens, 2014-09-25 This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms |
| agile data science download: Agile Data Science 2.0 Russell Jurney, 2017 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they're to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You'll learn an iterative approach that lets you quickly change the kind of analysis you're doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track. |
| agile data science download: Agile Metrics in Action Christopher Davis, 2015-07-31 Summary Agile Metrics in Action is a rich resource for agile teams that aim to use metrics to objectively measure performance. You'll learn how to gather data that really counts, along with how to effectively analyze and act upon the results. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book The iterative nature of agile development is perfect for experience-based, continuous improvement. Tracking systems, test and build tools, source control, continuous integration, and other built-in parts of a project lifecycle throw off a wealth of data you can use to improve your products, processes, and teams. The question is, how to do it? Agile Metrics in Action teaches you how. This practical book is a rich resource for an agile team that aims to use metrics to objectively measure performance. You'll learn how to gather the data that really count, along with how to effectively analyze and act upon the results. Along the way, you'll discover techniques all team members can use for better individual accountability and team performance. Practices in this book will work with any development process or tool stack. For code-based examples, this book uses Groovy, Grails, and MongoDB. What's Inside Use the data you generate every day from CI and Scrum Improve communication, productivity, transparency, and morale Objectively measure performance Make metrics a natural byproduct of your development process About the Author Christopher Davis has been a software engineer and team leader for over 15 years. He has led numerous teams to successful delivery using agile methodologies. Table of Contents PART 1 MEASURING AGILE TEAMS Measuring agile performance Observing a live project PART 2 COLLECTING AND ANALYZING YOUR TEAM'S DATA Trends and data from project-tracking systems Trends and data from source control Trends and data from CI and deployment servers Data from your production systems PART 3 APPLYING METRICS TO YOUR TEAMS, PROCESSES, AND SOFTWARE Working with the data you're collecting: the sum of the parts Measuring the technical quality of your software Publishing metrics Measuring your team against the agile principles |
什么是 Agile Software Development（敏捷软件开发）？ - 知乎
Apr 16, 2014 · 既然题主问的是“Agile Methodology”,那么便应该比限定在“软件开发”领域要更加宽泛。本回答从“敏捷开发”出发，尝试解读究竟什么才是“敏捷”。 一、从“敏捷开发”说起 “敏捷” …
什么是芯片领域的“敏捷设计（Agile Development - 知乎
什么是芯片领域的“敏捷设计（Agile Development）”？ 引用矽说公众号对DARPA资助项目的解说；也有提到RISCV，CHISEL等字眼。 敏捷设计与超高效计算芯片，DARPA为未来半导体发 …
请问路由器双频合一开了好还是不开好？ - 知乎
说实在的。。。这个问题要看具体场景，没什么确定性的答案。就我自己而言，一般都是开着的。除非是我自己这边设备很多，要做隔离优化网络的时候，否则不会手动去把双频分开来。 双 …
什么是 Agile Software Development（敏捷软件开发）？ - 知乎
Apr 16, 2014 · 既然题主问的是“Agile Methodology”,那么便应该比限定在“软件开发”领域要更加宽泛。本回答从“敏捷开发”出发，尝试解读究竟什么才是“敏捷”。 一、从“敏捷开发”说起 “敏捷”概 …
什么是芯片领域的“敏捷设计（Agile Development - 知乎
什么是芯片领域的“敏捷设计（Agile Development）”？ 引用矽说公众号对DARPA资助项目的解说；也有提到RISCV，CHISEL等字眼。 敏捷设计与超高效计算芯片，DARPA为未来半导体发 …
请问路由器双频合一开了好还是不开好？ - 知乎
说实在的。。。这个问题要看具体场景，没什么确定性的答案。就我自己而言，一般都是开着的。除非是我自己这边设备很多，要做隔离优化网络的时候，否则不会手动去把双频分开来。 双 …
