Project Data Tech

Download Project Data Tech full books in PDF, epub, and Kindle. Read online Project Data Tech ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

The Data-Driven Project Manager

Author : Mario Vanhoucke
Publisher : Apress
ISBN 13 : 1484234987
Total Pages : 164 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis The Data-Driven Project Manager by : Mario Vanhoucke

Download or read book The Data-Driven Project Manager written by Mario Vanhoucke and published by Apress. This book was released on 2018-03-27 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data Analytics in Project Management

Author : Seweryn Spalek
Publisher : Taylor & Francis Group/CRC Press
ISBN 13 : 1138307289
Total Pages : 11 pages
Book Rating : 4.1/5 (383 download)

DOWNLOAD NOW!

Book Synopsis Data Analytics in Project Management by : Seweryn Spalek

Download or read book Data Analytics in Project Management written by Seweryn Spalek and published by Taylor & Francis Group/CRC Press. This book was released on 2019-01-01 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Introduction to Data Technologies

Author : Paul Murrell
Publisher : CRC Press
ISBN 13 : 1420065181
Total Pages : 445 pages
Book Rating : 4.4/5 (2 download)

DOWNLOAD NOW!

Book Synopsis Introduction to Data Technologies by : Paul Murrell

Download or read book Introduction to Data Technologies written by Paul Murrell and published by CRC Press. This book was released on 2009-02-23 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei

Executing Data Quality Projects

Author : Danette McGilvray
Publisher : Academic Press
ISBN 13 : 0128180161
Total Pages : 376 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Executing Data Quality Projects by : Danette McGilvray

Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Academic Press. This book was released on 2021-05-27 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach Contains real examples from around the world, gleaned from the author’s consulting practice and from those who implemented based on her training courses and the earlier edition of the book Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online

Big Data Technologies and Applications

Author : Borko Furht
Publisher : Springer
ISBN 13 : 3319445502
Total Pages : 405 pages
Book Rating : 4.3/5 (194 download)

DOWNLOAD NOW!

Book Synopsis Big Data Technologies and Applications by : Borko Furht

Download or read book Big Data Technologies and Applications written by Borko Furht and published by Springer. This book was released on 2016-09-16 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Managing Data Science

Author : Kirill Dubovikov
Publisher : Packt Publishing Ltd
ISBN 13 : 1838824561
Total Pages : 276 pages
Book Rating : 4.8/5 (388 download)

DOWNLOAD NOW!

Book Synopsis Managing Data Science by : Kirill Dubovikov

Download or read book Managing Data Science written by Kirill Dubovikov and published by Packt Publishing Ltd. This book was released on 2019-11-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Getting Data Science Done

Author : John Hawkins
Publisher : Business Expert Press
ISBN 13 : 1637422784
Total Pages : 240 pages
Book Rating : 4.6/5 (374 download)

DOWNLOAD NOW!

Book Synopsis Getting Data Science Done by : John Hawkins

Download or read book Getting Data Science Done written by John Hawkins and published by Business Expert Press. This book was released on 2022-08-26 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting Data Science Done outlines the essential stages in running successful data science projects. Data science is a field that synthesizes statistics, computer science and business analytics to deliver results that can impact almost any type of process or organization. Data science is also an evolving technical discipline, whose practice is full of pitfalls and potential problems for managers, stakeholders and practitioners. Many organizations struggle to consistently deliver results with data science due to a wide range of issues, including knowledge barriers, problem framing, organizational change and integration with IT and engineering. Getting Data Science Done outlines the essential stages in running successful data science projects. The book provides comprehensive guidelines to help you identify potential issues and then a range of strategies for mitigating them. The book is organized as a sequential process allowing the reader to work their way through a project from an initial idea all the way to a deployed and integrated product.

Agile Data Warehousing Project Management

Author : Ralph Hughes
Publisher : Newnes
ISBN 13 : 0123965179
Total Pages : 379 pages
Book Rating : 4.1/5 (239 download)

DOWNLOAD NOW!

Book Synopsis Agile Data Warehousing Project Management by : Ralph Hughes

Download or read book Agile Data Warehousing Project Management written by Ralph Hughes and published by Newnes. This book was released on 2012-12-28 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data Teams

Author : Jesse Anderson
Publisher : Apress
ISBN 13 : 9781484262276
Total Pages : 294 pages
Book Rating : 4.2/5 (622 download)

DOWNLOAD NOW!

Book Synopsis Data Teams by : Jesse Anderson

Download or read book Data Teams written by Jesse Anderson and published by Apress. This book was released on 2020-10-03 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. What You Will Learn Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects Who This Book Is For Management, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects which may be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place. This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.

Project Management Analytics

Author : Harjit Singh
Publisher : FT Press
ISBN 13 : 0134190491
Total Pages : 412 pages
Book Rating : 4.1/5 (341 download)

DOWNLOAD NOW!

Book Synopsis Project Management Analytics by : Harjit Singh

Download or read book Project Management Analytics written by Harjit Singh and published by FT Press. This book was released on 2015-11-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: To manage projects, you must not only control schedules and costs: you must also manage growing operational uncertainty. Today’s powerful analytics tools and methods can help you do all of this far more successfully. In Project Management Analytics, Harjit Singh shows how to bring greater evidence-based clarity and rationality to all your key decisions throughout the full project lifecycle. Singh identifies the components and characteristics of a good project decision and shows how to improve decisions by using predictive, prescriptive, statistical, and other methods. You’ll learn how to mitigate risks by identifying meaningful historical patterns and trends; optimize allocation and use of scarce resources within project constraints; automate data-driven decision-making processes based on huge data sets; and effectively handle multiple interrelated decision criteria. Singh also helps you integrate analytics into the project management methods you already use, combining today’s best analytical techniques with proven approaches such as PMI PMBOK® and Lean Six Sigma. Project managers can no longer rely on vague impressions or seat-of-the-pants intuition. Fortunately, you don’t have to. With Project Management Analytics, you can use facts, evidence, and knowledge—and get far better results. Achieve efficient, reliable, consistent, and fact-based project decision-making Systematically bring data and objective analysis to key project decisions Avoid “garbage in, garbage out” Properly collect, store, analyze, and interpret your project-related data Optimize multi-criteria decisions in large group environments Use the Analytic Hierarchy Process (AHP) to improve complex real-world decisions Streamline projects the way you streamline other business processes Leverage data-driven Lean Six Sigma to manage projects more effectively

Foundations for Architecting Data Solutions

Author : Ted Malaska
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492038695
Total Pages : 196 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Foundations for Architecting Data Solutions by : Ted Malaska

Download or read book Foundations for Architecting Data Solutions written by Ted Malaska and published by "O'Reilly Media, Inc.". This book was released on 2018-08-29 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect

Data Analytics for Engineering and Construction Project Risk Management

Author : Ivan Damnjanovic
Publisher : Springer
ISBN 13 : 3030142515
Total Pages : 379 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Data Analytics for Engineering and Construction Project Risk Management by : Ivan Damnjanovic

Download or read book Data Analytics for Engineering and Construction Project Risk Management written by Ivan Damnjanovic and published by Springer. This book was released on 2019-05-23 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.

Project data summaries

Author : Systems Consultants, inc
Publisher :
ISBN 13 :
Total Pages : 80 pages
Book Rating : 4.:/5 (312 download)

DOWNLOAD NOW!

Book Synopsis Project data summaries by : Systems Consultants, inc

Download or read book Project data summaries written by Systems Consultants, inc and published by . This book was released on 1976 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Feminism

Author : Catherine D'Ignazio
Publisher : MIT Press
ISBN 13 : 0262358530
Total Pages : 328 pages
Book Rating : 4.2/5 (623 download)

DOWNLOAD NOW!

Book Synopsis Data Feminism by : Catherine D'Ignazio

Download or read book Data Feminism written by Catherine D'Ignazio and published by MIT Press. This book was released on 2020-03-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

Managing Machine Learning Projects

Author : Simon Thompson
Publisher : Simon and Schuster
ISBN 13 : 1638352062
Total Pages : 270 pages
Book Rating : 4.6/5 (383 download)

DOWNLOAD NOW!

Book Synopsis Managing Machine Learning Projects by : Simon Thompson

Download or read book Managing Machine Learning Projects written by Simon Thompson and published by Simon and Schuster. This book was released on 2023-07-25 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including: Understanding an ML project’s requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviors for managing the ethical implications of ML technology Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You’ll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value—read this book to make sure your project is a success. What's Inside Set up infrastructure and resource a team Bring data resources into a project Accurately estimate time and effort Evaluate which models to adopt for delivery Integrate models into effective applications About the Reader For anyone interested in better management of machine learning projects. No technical skills required. About the Author Simon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies. Table of Contents 1 Introduction: Delivering machine learning projects is hard; let’s do it better 2 Pre-project: From opportunity to requirements 3 Pre-project: From requirements to proposal 4 Getting started 5 Diving into the problem 6 EDA, ethics, and baseline evaluations 7 Making useful models with ML 8 Testing and selection 9 Sprint 3: system building and production 10 Post project (sprint O)

Big Data Analytics Project Management

Author : Tiffani Crawford
Publisher :
ISBN 13 : 9781492795391
Total Pages : 126 pages
Book Rating : 4.7/5 (953 download)

DOWNLOAD NOW!

Book Synopsis Big Data Analytics Project Management by : Tiffani Crawford

Download or read book Big Data Analytics Project Management written by Tiffani Crawford and published by . This book was released on 2013-10-01 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics technologies offer many great benefits along with challenges for project management professionals. Beyond common misconceptions about Big Data Analytics, there are many obstacles to business value. Project managers must be experts in the technologies and exploratory methodologies.

Resources in Education

Author :
Publisher :
ISBN 13 :
Total Pages : 334 pages
Book Rating : 4.0/5 ( download)

DOWNLOAD NOW!

Book Synopsis Resources in Education by :

Download or read book Resources in Education written by and published by . This book was released on 1995 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: