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Agile Methodology for Data Science Projects: Navigating the Uncertainties of Data Discovery
By Dr. Anya Sharma, PhD
Dr. Anya Sharma is a seasoned data scientist with over 15 years of experience in the industry, specializing in applying agile methodologies to complex data projects. She holds a PhD in Computer Science from Stanford University and has published numerous articles on data science best practices.
Published by: Data Science Digest, a leading online publication dedicated to providing insightful analysis and practical advice for data scientists and industry professionals. Data Science Digest is known for its rigorous editorial standards and commitment to delivering high-quality, evidence-based content.
Edited by: Mark Johnson, a veteran editor with 20 years of experience in technical publishing and a deep understanding of data science principles.
Introduction:
The traditional waterfall approach to software development, with its rigid phases and sequential processes, often struggles to adapt to the inherent uncertainties and iterative nature of data science projects. This is where agile methodology for data science projects emerges as a transformative solution. By embracing iterative development, continuous feedback, and close collaboration, agile methodologies empower data science teams to deliver impactful results in a dynamic and unpredictable environment. This article will delve into the benefits and challenges of implementing agile in data science, exploring its impact on the industry's evolution.
H1: Why Agile Matters in Data Science
Data science projects are rarely straightforward. The initial problem definition may evolve as data is explored and insights are uncovered. Unforeseen challenges, such as data quality issues or unexpected algorithmic limitations, are commonplace. The agile methodology for data science projects allows for flexibility and adaptation. Instead of rigidly adhering to a predefined plan, agile embraces iterative cycles (sprints) focused on delivering incremental value. This iterative approach minimizes risks, facilitates early detection of problems, and enables quicker course corrections.
H2: Core Agile Principles in Data Science
Several core agile principles are particularly relevant to data science:
Iterative Development: Breaking down the project into smaller, manageable sprints allows for frequent testing and feedback, leading to better alignment with business needs.
Continuous Integration and Continuous Delivery (CI/CD): Automating the build, test, and deployment process ensures faster iterations and reduces manual errors. This is critical for data science, where model retraining and deployment are frequent.
Collaboration and Communication: Agile emphasizes close collaboration between data scientists, engineers, stakeholders, and business users. This ensures everyone is aligned on the project goals and progress.
Data-Driven Decision Making: Agile uses data to track progress, identify bottlenecks, and make informed decisions about the project's direction.
Prioritization: Agile techniques, such as user stories and story mapping, help prioritize tasks based on their value and impact.
H3: Implementing Agile: Challenges and Best Practices
While the benefits of adopting agile methodology for data science projects are clear, implementation requires careful planning and consideration:
Defining Success Metrics: Establishing clear, measurable goals is crucial for tracking progress and ensuring alignment with business objectives.
Managing Data Complexity: Handling large and diverse datasets requires efficient data management strategies and appropriate tooling.
Collaboration across Disciplines: Effective communication and collaboration are essential to bridge the gap between data scientists, engineers, and business stakeholders.
Dealing with Uncertainty: Agile's iterative nature allows for adaptation to unexpected challenges, but effective risk management is still crucial.
H4: The Impact on the Data Science Industry
The adoption of agile methodology for data science projects is transforming the industry in several ways:
Faster Time to Market: Iterative development allows for quicker delivery of valuable insights and solutions.
Increased Business Value: Close collaboration ensures the projects align with business needs and deliver tangible results.
Improved Project Success Rates: Early detection of problems and continuous feedback minimize risks and improve project outcomes.
Enhanced Collaboration: Agile promotes a more collaborative and communicative work environment.
H5: The Future of Agile in Data Science
As the data science field continues to evolve, the demand for agile methodologies will only grow. The increasing complexity of data and the need for faster time to market will make agile an indispensable part of the data science lifecycle. Future developments will likely focus on integrating agile with advanced data science techniques, such as machine learning and deep learning, to further enhance efficiency and effectiveness.
Conclusion:
The adoption of agile methodology for data science projects is no longer a luxury but a necessity. By embracing iterative development, continuous feedback, and close collaboration, organizations can unlock the full potential of their data science initiatives and deliver impactful results in a rapidly changing environment. The challenges involved in implementation are surmountable with careful planning and a commitment to agile principles. The future of data science is inextricably linked to the widespread adoption and refinement of agile methodologies.
FAQs:
1. What are the key differences between Agile and Waterfall in data science? Agile is iterative, allowing for adjustments; Waterfall is sequential and less adaptable.
2. Which Agile frameworks are best suited for data science? Scrum and Kanban are popular choices, often used in combination.
3. How can I measure the success of an Agile data science project? Use metrics like sprint velocity, defect rate, and stakeholder satisfaction.
4. What are the common challenges in implementing Agile in data science? Data complexity, interdisciplinary collaboration, and managing uncertainty are key challenges.
5. How can I improve collaboration in an Agile data science team? Use daily stand-ups, sprint reviews, and retrospectives for effective communication.
6. What tools can support Agile data science projects? Jira, Trello, and Azure DevOps are commonly used.
7. How do I handle changing requirements in an Agile data science project? Embrace the flexibility of Agile and incorporate changes in subsequent sprints.
8. How can Agile help reduce the risk of project failure in data science? Iterative development and continuous feedback allow for early problem detection and correction.
9. Is Agile suitable for all data science projects? While highly beneficial for most, its suitability depends on project size, complexity, and team structure. Smaller projects might not require the full Agile framework.
Related Articles:
1. "Agile Data Science: A Practical Guide": A step-by-step guide on implementing agile principles in data science projects, including practical examples and templates.
2. "Scaling Agile for Large Data Science Projects": This article discusses strategies for adapting agile methodologies to handle the complexities of large-scale data science initiatives.
3. "Agile and DevOps for Data Science": Explores the integration of agile and DevOps practices to streamline the data science pipeline.
4. "The Role of Data Visualization in Agile Data Science": This article highlights the importance of data visualization in facilitating communication and feedback within agile data science teams.
5. "Using Scrum for Data Science Projects": A detailed guide on applying the Scrum framework to data science projects.
6. "Kanban for Data Scientists: A Flexible Approach": This article focuses on the Kanban method and its advantages for data science.
7. "Agile Data Science: Measuring Success and ROI": This piece examines key metrics for evaluating the success of agile data science projects.
8. "Addressing Data Quality Issues in Agile Data Science": This article deals with the challenges of maintaining data quality within an agile framework.
9. "Agile Data Science and the Cloud": This explores how cloud-based platforms can support agile data science workflows.
| agile methodology for data science projects: 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 methodology for data science projects: 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 methodology for data science projects: Agile Analytics Ken Collier, 2012 Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Collier's techniques offer optimal value whether your projects involve back-end data management, front-end business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right now--whether you're an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better results--and have fun along the way. |
| agile methodology for data science projects: Agile Data Warehousing Project Management Ralph Hughes, 2012-12-28 You have to make sense of enormous amounts of data, and while the notion of agile data warehousing might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting project costs in half. Bring this highly effective technique to your organization with the wisdom of agile data warehousing expert Ralph Hughes. Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the project room to build a serious data mart. Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. - Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track - Includes strategies for getting accurate and actionable requirements from a team's business partner - Revolutionary estimating techniques that make forecasting labor far more understandable and accurate - Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties - Enables you and your teams to start simple and progress steadily to world-class performance levels |
| agile methodology for data science projects: Agile Machine Learning Eric Carter, Matthew Hurst, 2019-08-21 Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations Who This Book Is For Anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. |
| agile methodology for data science projects: Agile Processes in Software Engineering and Extreme Programming – Workshops Rashina Hoda, 2019-08-30 This open access book constitutes the research workshops, doctoral symposium and panel summaries presented at the 20th International Conference on Agile Software Development, XP 2019, held in Montreal, QC, Canada, in May 2019. 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 2019 provided an informal environment to network, share, and discover trends in Agile for the next 20 years. Research papers and talks submissions were invited for the three XP 2019 research workshops, namely, agile transformation, autonomous teams, and large scale agile. This book includes 15 related papers. In addition, a summary for each of the four panels at XP 2019 is included. The panels were on security and privacy; the impact of the agile manifesto on culture, education, and software practices; business agility – agile’s next frontier; and Agile – the next 20 years. |
| agile methodology for data science projects: Becoming an Agile Software Architect Rajesh R V, 2021-03-19 A guide to successfully operating in a lean-agile organization for solutions architects and enterprise architects Key FeaturesDevelop the right combination of processes and technical excellence to address architectural challengesExplore a range of architectural techniques to modernize legacy systemsDiscover how to design and continuously improve well-architected sustainable softwareBook Description Many organizations have embraced Agile methodologies to transform their ability to rapidly respond to constantly changing customer demands. However, in this melee, many enterprises often neglect to invest in architects by presuming architecture is not an intrinsic element of Agile software development. Since the role of an architect is not pre-defined in Agile, many organizations struggle to position architects, often resulting in friction with other roles or a failure to provide a clear learning path for architects to be productive. This book guides architects and organizations through new Agile ways of incrementally developing the architecture for delivering an uninterrupted, continuous flow of values that meets customer needs. You'll explore various aspects of Agile architecture and how it differs from traditional architecture. The book later covers Agile architects' responsibilities and how architects can add significant value by positioning themselves appropriately in the Agile flow of work. Through examples, you'll also learn concepts such as architectural decision backlog,the last responsible moment, value delivery, architecting for change, DevOps, and evolutionary collaboration. By the end of this Agile book, you'll be able to operate as an architect in Agile development initiatives and successfully architect reliable software systems. What you will learnAcquire clarity on the duties of architects in Agile developmentUnderstand architectural styles such as domain-driven design and microservicesIdentify the pitfalls of traditional architecture and learn how to develop solutionsUnderstand the principles of value and data-driven architectureDiscover DevOps and continuous delivery from an architect's perspectiveAdopt Lean-Agile documentation and governanceDevelop a set of personal and interpersonal qualitiesFind out how to lead the transformation to achieve organization-wide agilityWho this book is for This agile study guide is for architects currently working on agile development projects or aspiring to work on agile software delivery, irrespective of the methodology they are using. You will also find this book useful if you're a senior developer or a budding architect looking to understand an agile architect's role by embracing agile architecture strategies and a lean-agile mindset. To understand the concepts covered in this book easily, you need to have prior knowledge of basic agile development practices. |
| agile methodology for data science projects: Agile Data Warehousing Ralph Hughes, 2008-07-14 Contains a six-stage plan for starting new warehouse projects and guiding programmers step-by-step until they become a world-class, Agile development team. It describes also how to avoid or contain the fierce opposition that radically new methods can encounter from the traditionally-minded IS departments found in many large companies. |
| agile methodology for data science projects: Research Anthology on Agile Software, Software Development, and Testing Management Association, Information Resources, 2021-11-26 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 methodology for data science projects: Intelligence-Based Medicine Anthony C. Chang, 2020-06-27 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. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare |
| agile methodology for data science projects: 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 methodology for data science projects: 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 methodology for data science projects: Clean Agile Robert C. Martin, 2019-09-12 Agile Values and Principles for a New Generation “In the journey to all things Agile, Uncle Bob has been there, done that, and has the both the t-shirt and the scars to show for it. This delightful book is part history, part personal stories, and all wisdom. If you want to understand what Agile is and how it came to be, this is the book for you.” –Grady Booch “Bob’s frustration colors every sentence of Clean Agile, but it’s a justified frustration. What is in the world of Agile development is nothing compared to what could be. This book is Bob’s perspective on what to focus on to get to that ‘what could be.’ And he’s been there, so it’s worth listening.” –Kent Beck “It’s good to read Uncle Bob’s take on Agile. Whether just beginning, or a seasoned Agilista, you would do well to read this book. I agree with almost all of it. It’s just some of the parts make me realize my own shortcomings, dammit. It made me double-check our code coverage (85.09%).” –Jon Kern Nearly twenty years after the Agile Manifesto was first presented, the legendary Robert C. Martin (“Uncle Bob”) reintroduces Agile values and principles for a new generation–programmers and nonprogrammers alike. Martin, author of Clean Code and other highly influential software development guides, was there at Agile’s founding. Now, in Clean Agile: Back to Basics, he strips away misunderstandings and distractions that over the years have made it harder to use Agile than was originally intended. Martin describes what Agile is in no uncertain terms: a small discipline that helps small teams manage small projects . . . with huge implications because every big project is comprised of many small projects. Drawing on his fifty years’ experience with projects of every conceivable type, he shows how Agile can help you bring true professionalism to software development. Get back to the basics–what Agile is, was, and should always be Understand the origins, and proper practice, of SCRUM Master essential business-facing Agile practices, from small releases and acceptance tests to whole-team communication Explore Agile team members’ relationships with each other, and with their product Rediscover indispensable Agile technical practices: TDD, refactoring, simple design, and pair programming Understand the central roles values and craftsmanship play in your Agile team’s success If you want Agile’s true benefits, there are no shortcuts: You need to do Agile right. Clean Agile: Back to Basics will show you how, whether you’re a developer, tester, manager, project manager, or customer. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| agile methodology for data science projects: The Data-Driven Project Manager Mario Vanhoucke, 2018-03-27 Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools. The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles. Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows: Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project’s time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project’s time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project’s performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used. What You'll Learn Implement a data-driven project management methodology (also known as dynamic scheduling) which allows project managers to plan, monitor, and control projects while delivering them on time and within budget Study different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM) Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project control Who This Book Is For Project managers looking to learn data-driven project management (or dynamic scheduling) via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles |
| agile methodology for data science projects: Development Methodologies for Big Data Analytics Systems Manuel Mora, Fen Wang, Jorge Marx Gomez, Hector Duran-Limon, 2023-11-03 This book presents research in big data analytics (BDA) for business of all sizes. The authors analyze problems presented in the application of BDA in some businesses through the study of development methodologies based on the three approaches – 1) plan-driven, 2) agile and 3) hybrid lightweight. The authors first describe BDA systems and how they emerged with the convergence of Statistics, Computer Science, and Business Intelligent Analytics with the practical aim to provide concepts, models, methods and tools required for exploiting the wide variety, volume, and velocity of available business internal and external data - i.e. Big Data – and provide decision-making value to decision-makers. The book presents high-quality conceptual and empirical research-oriented chapters on plan-driven, agile, and hybrid lightweight development methodologies and relevant supporting topics for BDA systems suitable to be used for large-, medium-, and small-sized business organizations. |
| agile methodology for data science projects: Agile Project Management with Scrum Ken Schwaber, 2004-02-11 The rules and practices for Scrum—a simple process for managing complex projects—are few, straightforward, and easy to learn. But Scrum’s simplicity itself—its lack of prescription—can be disarming, and new practitioners often find themselves reverting to old project management habits and tools and yielding lesser results. In this illuminating series of case studies, Scrum co-creator and evangelist Ken Schwaber identifies the real-world lessons—the successes and failures—culled from his years of experience coaching companies in agile project management. Through them, you’ll understand how to use Scrum to solve complex problems and drive better results—delivering more valuable software faster. Gain the foundation in Scrum theory—and practice—you need to: Rein in even the most complex, unwieldy projects Effectively manage unknown or changing product requirements Simplify the chain of command with self-managing development teams Receive clearer specifications—and feedback—from customers Greatly reduce project planning time and required tools Build—and release—products in 30-day cycles so clients get deliverables earlier Avoid missteps by regularly inspecting, reporting on, and fine-tuning projects Support multiple teams working on a large-scale project from many geographic locations Maximize return on investment! |
| agile methodology for data science projects: 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 methodology for data science projects: Agile and Iterative Development Craig Larman, 2004 This is the definitive guide for managers and students to agile and iterativedevelopment methods: what they are, how they work, how to implement them, andwhy they should. |
| agile methodology for data science projects: Agile Project Management For Dummies Mark C. Layton, Steven J. Ostermiller, 2017-09-05 Flex your project management muscle Agile project management is a fast and flexible approach to managing all projects, not just software development. By learning the principles and techniques in this book, you'll be able to create a product roadmap, schedule projects, and prepare for product launches with the ease of Agile software developers. You'll discover how to manage scope, time, and cost, as well as team dynamics, quality, and risk of every project. As mobile and web technologies continue to evolve rapidly, there is added pressure to develop and implement software projects in weeks instead of months—and Agile Project Management For Dummies can help you do just that. Providing a simple, step-by-step guide to Agile project management approaches, tools, and techniques, it shows product and project managers how to complete and implement projects more quickly than ever. Complete projects in weeks instead of months Reduce risk and leverage core benefits for projects Turn Agile theory into practice for all industries Effectively create an Agile environment Get ready to grasp and apply Agile principles for faster, more accurate development. |
| agile methodology for data science projects: 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 methodology for data science projects: 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 methodology for data science projects: Agile Project Delivery Aaron A. Blair, 2020-12-18 Agile Project Delivery reviews how different Agile methods can be applied to project delivery in complex corporate environments beyond the Agile Manifesto’s original scope of software development. Taking readers through a typical project lifecycle, the text demonstrates how Agile techniques can be applied to each phase of a project using valuable tools and examples. Agile Project Delivery covers various approaches that are used across the many methodologies and frameworks that are part of the Agile family, including Scrum, XP, and Crystal, as well as some of Agile’s influences, such as Lean and Kanban. Agile Project Delivery also provides readers with advanced instructions for using Atlassian’s industry-leading Agile software, Jira. Bridging the gap between Agile methodology and application, this concise guide features practical delivery approaches, engaging case studies, useful templates to assist in Agile application, and chapter discussion questions to reinforce understanding on how to harness the benefits of Agile. With a focus on settings outside of software development and an accessible, pragmatic approach, Agile Project Delivery is an invaluable resource for students in any project management course, as well as for both aspiring and experienced project practitioners. |
| agile methodology for data science projects: Why Greatness Cannot Be Planned Kenneth O. Stanley, Joel Lehman, 2015-05-05 Why does modern life revolve around objectives? From how science is funded, to improving how children are educated -- and nearly everything in-between -- our society has become obsessed with a seductive illusion: that greatness results from doggedly measuring improvement in the relentless pursuit of an ambitious goal. In Why Greatness Cannot Be Planned, Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. They make the case that great achievement can't be bottled up into mechanical metrics; that innovation is not driven by narrowly focused heroic effort; and that we would be wiser (and the outcomes better) if instead we whole-heartedly embraced serendipitous discovery and playful creativity. Controversial at its heart, yet refreshingly provocative, this book challenges readers to consider life without a destination and discovery without a compass. |
| agile methodology for data science projects: Agile Software Development Thomas Stober, Uwe Hansmann, 2009-10-03 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 methodology for data science projects: 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 methodology for data science projects: Project Management Waterfall-Agile-It-Data Science Dr. Festus Elleh PhD PMP PMI-ACP, 2023-03-22 This book is intended to introduce learners to waterfall, agile, information technology, and data science project management methodologies. Readers will learn about the concepts, processes, tools, and techniques that are useful for executing projects in waterfall, agile information technology, and data science environments. The objective is for learners to become contributors to the field of project management and deploy a structured approach to managing projects. Learners who read this book will be able to think critically about the concepts and practices of project management and perform exceptionally well in the PMP and PMI-ACP examinations. |
| agile methodology for data science projects: Accelerating Process Improvement Using Agile Techniques Deb Jacobs, 2005-12-16 Accelerating Process Improvement Using Agile Techniques explains how agile programming is applied to standard process improvement. By applying agile techniques, IT organizations can speed up process improvement initiatives, minimize the resources these initiatives require, and maximize the benefits of process improvement. The book details st |
| agile methodology for data science projects: 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 methodology for data science projects: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book. |
| agile methodology for data science projects: 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 methodology for data science projects: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| agile methodology for data science projects: Agile Processes in Software Engineering and Extreme Programming Hubert Baumeister, Horst Lichter, Matthias Riebisch, 2017-04-12 This book is open access under a CC BY license. The volume constitutes the proceedings of the 18th International Conference on Agile Software Development, XP 2017, held in Cologne, Germany, in May 2017. The 14 full and 6 short papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: improving agile processes; agile in organization; and safety critical software. In addition, the volume contains 3 doctoral symposium papers (from 4 papers submitted). |
| agile methodology for data science projects: 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 methodology for data science projects: Practical DataOps Harvinder Atwal, 2019-12-09 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 LearnDevelop 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 methodology for data science projects: Lean-Agile Software Development Alan Shalloway, Guy Beaver, James R. Trott, 2009-10-22 Agile techniques have demonstrated immense potential for developing more effective, higher-quality software. However,scaling these techniques to the enterprise presents many challenges. The solution is to integrate the principles and practices of Lean Software Development with Agile’s ideology and methods. By doing so, software organizations leverage Lean’s powerful capabilities for “optimizing the whole” and managing complex enterprise projects. A combined “Lean-Agile” approach can dramatically improve both developer productivity and the software’s business value.In this book, three expert Lean software consultants draw from their unparalleled experience to gather all the insights, knowledge, and new skills you need to succeed with Lean-Agile development. Lean-Agile Software Development shows how to extend Scrum processes with an Enterprise view based on Lean principles. The authors present crucial technical insight into emergent design, and demonstrate how to apply it to make iterative development more effective. They also identify several common development “anti-patterns” that can work against your goals, and they offer actionable, proven alternatives. Lean-Agile Software Development shows how to Transition to Lean Software Development quickly and successfully Manage the initiation of product enhancements Help project managers work together to manage product portfolios more effectively Manage dependencies across the software development organization and with its partners and colleagues Integrate development and QA roles to improve quality and eliminate waste Determine best practices for different software development teams The book’s companion Web site, www.netobjectives.com/lasd, provides updates, links to related materials, and support for discussions of the book’s content. |
| agile methodology for data science projects: Advances in Design, Simulation and Manufacturing IV Vitalii Ivanov, Justyna Trojanowska, Ivan Pavlenko, Jozef Zajac, Dragan Peraković, 2021-05-25 This book reports on topics at the interface between manufacturing and materials engineering, with a special emphasis on product design and advanced manufacturing processes, intelligent solutions for Industry 4.0, covers topics in ICT for engineering education, describes the numerical simulation and experimental studies of milling, honing, burnishing, grinding, boring, and turning, as well as the development and implementation of advanced materials. Based on the 4th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange (DSMIE-2021), held on June 8-11, 2021, in Lviv, Ukraine, this first volume of a 2-volume set provides academics and professionals with extensive information on trends, technologies, challenges and practice-oriented experience in the above-mentioned areas. |
| agile methodology for data science projects: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| agile methodology for data science projects: Scrum Project Management Kim H. Pries, Jon M. Quigley, 2010-08-17 Originally created for agile software development, scrum provides project managers with the flexibility needed to meet ever-changing consumer demands. Presenting a modified version of the agile software development framework, Scrum Project Management introduces Scrum basics and explains how to apply this adaptive technique to effectively manage a w |
| agile methodology for data science projects: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| agile methodology for data science projects: Introducing Data Science Davy Cielen, Arno Meysman, 2016-05-02 Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user |
AGILE PRACTICES IN DATA SCIENCE AND DATA ANALYTICS …
Therefore, this paper focuses on agile and data science/analytics projects. The research questions that this study aims to address are: Which agile frameworks and principles are used …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, and over all, reproducible science.
Applying Scrum in Data Science Projects - research.ou.nl
One well known Agile method often applied in data science projects is Scrum. Scrum is characterized by time-boxed sprints to deliver incremental value and consists of different …
AnalyticsOps for Data Science - Methodologies to Improve …
the agile software development methodology (often referred to simply as agile) and how its principles may be applied to data science are best understood from how it evolved. A. The …
BUILDING AN AGILE DATA SCIENCE PROCESS FOR …
Thus, the objective of this research is to build a new process model for data science to enable data science projects to start working with soft-ware development teams from the early stages …
DataOps: An Agile Methodology for Data-Driven …
An agile approach to data science embraces collaboration and creativity, bringing all elements of big data analysis together to make results visible, shareable, reproducible, and standardized. …
Agile Data Science - Online Tutorials Library
Agile data science is an approach of using data science with agile methodology for web application development. It focusses on the output of the data science process suitable for …
Implementing an agile lifecycle model for Data Science …
Abstract — This document shows an investigation to determine the efficiency or benefits of implementing an agile model according to data science projects, helping to companies or …
Model for Assessment of Agile Methodology for …
In this book the author has described agile principles tailored for project management of Datawarehouse and Business Intelligence development. Also a detailed description of …
Being Agile in a Data Science Project - Springer
Abstract. Applying agile practices in data science requires adapta-tions. This paper describes challenges and lessons learned in two applied machine learning projects developed in the XP …
Designing a Robust Data Platform: A Comprehensive Study on …
action, this thesis delves into the incorporation of agile principles into data engineering workflows and highlights its advantages and challenges while delivering large-scale data projects by …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tool in academia, to make a more responsible, and over all, reproducible science.
Agile Data Science Building Data Analytics Applications With …
Agile methodologies, known for their iterative and incremental approach, perfectly align with the exploratory nature of data science. Instead of lengthy upfront design, we embrace short …
DataOps: An Agile Methodology for Data-Driven …
DATAOPS: AN AGILE METHODOLOGY FOR DATA-DRIVEN ORGANIZATIONS A Platform Approach Unlike the traditional waterfall model of product development, an agile approach to …
Machine Learning models to predict Agile Methodology …
The primary objective of this paper is to use machine learning to develop predictive models for Scrum adop-tion, identifying a preliminary model with the highest predic-tion accuracy. The …
Delight the Customer using Agile Transformation in Clinical
Some examples of agile use in data science are as follows: The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions …
Analysis of Software Engineering for Agile Machine Learning …
proposed a generic agile cycle for machine learning projects including continuously observing problems with existing ML models, defining new features, followed by training, testing
Agile Project Management Approach and its Use in Big Data …
Based on the evaluation and processing of data from the interview, we identify which of the principles of Agile Manifesto, can be used in the management of Big Data projects. …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, sustainable, and above all, reproducible science.
Data Management Challenges in Agile Software Projects: A …
Our results show that data management plays a pivotal role in agile software development, emphasizing key aspects such as data integration, data collection, data quality and data …
AGILE PRACTICES IN DATA SCIENCE AND DATA ANALYTICS …
Therefore, this paper focuses on agile and data science/analytics projects. The research questions that this study aims to address are: Which agile frameworks and principles are used …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, and over all, reproducible science.
Applying Scrum in Data Science Projects - research.ou.nl
One well known Agile method often applied in data science projects is Scrum. Scrum is characterized by time-boxed sprints to deliver incremental value and consists of different …
AnalyticsOps for Data Science - Methodologies to Improve …
the agile software development methodology (often referred to simply as agile) and how its principles may be applied to data science are best understood from how it evolved. A. The …
BUILDING AN AGILE DATA SCIENCE PROCESS FOR …
Thus, the objective of this research is to build a new process model for data science to enable data science projects to start working with soft-ware development teams from the early stages …
DataOps: An Agile Methodology for Data-Driven …
An agile approach to data science embraces collaboration and creativity, bringing all elements of big data analysis together to make results visible, shareable, reproducible, and standardized. …
Agile Data Science - Online Tutorials Library
Agile data science is an approach of using data science with agile methodology for web application development. It focusses on the output of the data science process suitable for …
Implementing an agile lifecycle model for Data Science …
Abstract — This document shows an investigation to determine the efficiency or benefits of implementing an agile model according to data science projects, helping to companies or …
Model for Assessment of Agile Methodology for …
In this book the author has described agile principles tailored for project management of Datawarehouse and Business Intelligence development. Also a detailed description of …
Being Agile in a Data Science Project - Springer
Abstract. Applying agile practices in data science requires adapta-tions. This paper describes challenges and lessons learned in two applied machine learning projects developed in the XP …
Designing a Robust Data Platform: A Comprehensive Study …
action, this thesis delves into the incorporation of agile principles into data engineering workflows and highlights its advantages and challenges while delivering large-scale data projects by …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tool in academia, to make a more responsible, and over all, reproducible science.
Agile Data Science Building Data Analytics Applications With …
Agile methodologies, known for their iterative and incremental approach, perfectly align with the exploratory nature of data science. Instead of lengthy upfront design, we embrace short …
DataOps: An Agile Methodology for Data-Driven …
DATAOPS: AN AGILE METHODOLOGY FOR DATA-DRIVEN ORGANIZATIONS A Platform Approach Unlike the traditional waterfall model of product development, an agile approach to …
Machine Learning models to predict Agile Methodology …
The primary objective of this paper is to use machine learning to develop predictive models for Scrum adop-tion, identifying a preliminary model with the highest predic-tion accuracy. The …
Delight the Customer using Agile Transformation in Clinical
Some examples of agile use in data science are as follows: The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions …
Analysis of Software Engineering for Agile Machine Learning …
proposed a generic agile cycle for machine learning projects including continuously observing problems with existing ML models, defining new features, followed by training, testing
Agile Project Management Approach and its Use in Big Data …
Based on the evaluation and processing of data from the interview, we identify which of the principles of Agile Manifesto, can be used in the management of Big Data projects. …
Agile (data) science: a (draft) manifesto - arXiv.org
In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, sustainable, and above all, reproducible science.
Data Management Challenges in Agile Software Projects: A …
Our results show that data management plays a pivotal role in agile software development, emphasizing key aspects such as data integration, data collection, data quality and data …
