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Author: Christian J. Ward
Publisher: Ward Pllc
ISBN: 9781732991705
Category : Big data
Languages : en
Pages : 220
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Book Description
"We have a ton of DATA, now how do we LEVERAGE it?" The data your company generates is enormously valuable. But without the right strategy, you'll never unlock that value - and you might even put your company at risk. Data Leverage is the first comprehensive book on the exploding opportunity of corporate data partnerships. The authors, Christian and Jay Ward, are experts -- a business strategist and a lawyer who, together, have executed hundreds of deals. This book has everything you need to make money from data, starting with the DataSmart Method(TM), a four-step process for building your data partnership strategy. You'll learn: A comprehensive process to >identify your data assets - both the data your company generates and the data about your company that others maintain. A systematic way to value those assets - so you can tell whether it makes more sense to barter them for other valuable data or build them into million-dollar revenue streams. A complete list of deal structures for data partnerships, including how to gain partners for innovative data streams and how to distribute data through large platforms and channels. An analysis of prudent measures you can take to protect your data, with detailed descriptions of how to write contracts and comply with regulations like Europe's GDPR. This book will open your eyes to the power of data with detailed descriptions of real deals. You'll see how companies turned unusual data streams - like aerial photographs of retailers' parking lots, results of customer sales calls, and even their own accounts receivable data - into valuable assets that boosted their companies' bottom lines. Your company is churning out data every day. Your marketing department is generating ads and leads; your HR department is evaluating resumes; your IT group is tracking customer databases and product information. But without a strategy, it's just a bunch of ones and zeroes. To leverage that data, you need to find the right partners, make the right deals, maintain privacy controls, and build contracts that will keep you safe and legal. You'll need the detailed advice in this book as you negotiate with big platforms like Bloomberg, Thomson-Reuters, Dun & Bradstreet, and Amazon. Don't build data partnerships without a detailed map. Data Leverage is the indispensable reference you need to plan for and negotiate data deals. Keep it close by, and you can get started building whole new sources of value for your company with the data you're generating every single day.
Author: Christian J. Ward
Publisher: Ward Pllc
ISBN: 9781732991705
Category : Big data
Languages : en
Pages : 220
Get Book
Book Description
"We have a ton of DATA, now how do we LEVERAGE it?" The data your company generates is enormously valuable. But without the right strategy, you'll never unlock that value - and you might even put your company at risk. Data Leverage is the first comprehensive book on the exploding opportunity of corporate data partnerships. The authors, Christian and Jay Ward, are experts -- a business strategist and a lawyer who, together, have executed hundreds of deals. This book has everything you need to make money from data, starting with the DataSmart Method(TM), a four-step process for building your data partnership strategy. You'll learn: A comprehensive process to >identify your data assets - both the data your company generates and the data about your company that others maintain. A systematic way to value those assets - so you can tell whether it makes more sense to barter them for other valuable data or build them into million-dollar revenue streams. A complete list of deal structures for data partnerships, including how to gain partners for innovative data streams and how to distribute data through large platforms and channels. An analysis of prudent measures you can take to protect your data, with detailed descriptions of how to write contracts and comply with regulations like Europe's GDPR. This book will open your eyes to the power of data with detailed descriptions of real deals. You'll see how companies turned unusual data streams - like aerial photographs of retailers' parking lots, results of customer sales calls, and even their own accounts receivable data - into valuable assets that boosted their companies' bottom lines. Your company is churning out data every day. Your marketing department is generating ads and leads; your HR department is evaluating resumes; your IT group is tracking customer databases and product information. But without a strategy, it's just a bunch of ones and zeroes. To leverage that data, you need to find the right partners, make the right deals, maintain privacy controls, and build contracts that will keep you safe and legal. You'll need the detailed advice in this book as you negotiate with big platforms like Bloomberg, Thomson-Reuters, Dun & Bradstreet, and Amazon. Don't build data partnerships without a detailed map. Data Leverage is the indispensable reference you need to plan for and negotiate data deals. Keep it close by, and you can get started building whole new sources of value for your company with the data you're generating every single day.
Author: Christoph Prinz
Publisher: Cuvillier Verlag
ISBN: 3736968027
Category : Business & Economics
Languages : en
Pages : 236
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Book Description
Smart sustainable mobility ecosystems promise to address society’s expectation of environmentally friendly on-demand mobility. While the technology stack to build such ecosystems is just around the corner in the form of connected, automated, and electric vehicles, strategies to deploy and operate such fleets in a coordinated manner must still be advanced. Most of such optimization challenges highly depend on the nature of customer demand, vehicle supply, and environmental influences. Hence, this dissertation investigates how available data streams from mobility ecosystems can be leveraged in Information Systems to solve related decision problems. The overarching goal of this work is to generate design knowledge to improve vehicle availability, provider profitability, and environmental sustainability for such ecosystems. Applying quantitative methods to real-world data from shared vehicle systems generates insights into the nature of demand and supply. Combining it with an analysis of empirical research on vehicle relocation algorithms builds the foundation for two artifact designs. The first artifact enables the development and simulation-based evaluation of operation modes for vehicle fleets. The second artifact enables artificial intelligence-based decision support for the vehicle rebalancing problem. The insights are finally incorporated and generalized to a nascent design theory on data-enabled operational decision-making in the context of smart sustainable mobility environments. The findings have multifaceted implications for researchers concerned with data-enabled value creation in Green IS, shared economy and smart mobility, and business analytics and data science. Furthermore, guidance for fleet providers to improve system attractiveness and for society to experience the potential amount of vehicle access without personal ownership is provided.
Author: Leo Anthony Celi
Publisher: Springer Nature
ISBN: 3030479943
Category : Medical
Languages : en
Pages : 471
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Book Description
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Author: Simon Walkowiak
Publisher: Packt Publishing Ltd
ISBN: 1786463725
Category : Computers
Languages : en
Pages : 498
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Book Description
Utilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market Who This Book Is For This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R. What You Will Learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform In Detail Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O. Style and approach This book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets.
Author: Maxime C. Cohen
Publisher: Springer Nature
ISBN: 3030858553
Category : Business & Economics
Languages : en
Pages : 166
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Book Description
From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.
Author: Paul Bambrick-Santoyo
Publisher: John Wiley & Sons
ISBN: 1119496659
Category : Education
Languages : en
Pages : 277
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Book Description
Build better schools by training better leaders A Principal Manager’s Guide to Leverage Leadership answers the question that district leaders have been asking across the country: if Leverage Leadership is a roadmap for principals on how to lead great schools, what can principal managers and districts do to support them on that path? A Principal Manager’s Guide to Leverage Leadership offers a step-by-step guide to coaching principals to the highest levels of achievement, and it is rooted in studying the most successful principal managers and districts across the country. It can be used by principal managers/supervisors, superintendents, district and state leadership, and principal training organizations to accelerate the growth of principals in your community. Used in conjunction with Leverage Leadership 2.0, this book identifies the key actions principal managers should take to create exceptional school leaders, integrating the seven levers of leadership into district culture from the principal manager on up. With a particular emphasis on the two “super-levers” of data-driven instruction and student culture, this book is packed with advice, professional development materials, and real-world videos of principal managers in action, offering principal managers a valuable resource for bringing about change. A Principal Manager’s Guide to Leverage Leadership introduces a new unifying approach that is also highlighted in Leverage Leadership 2.0: See It, Name It, Do It. It gives you the tools to See it (see models of effective practice and identify gaps), Name it (name concrete actions for improvement) and Do it (provide means to practice these action steps until a principal masters them) With A Principal Manager’s Guide to Leverage Leadership in hand, principal managers, superintendents and principal training organizations can facilitate district-wide and state-wide transformations and hasten the benefit to the students and community as a whole.
Author: Thomas W. Many
Publisher: Corwin Press
ISBN: 1483364771
Category : Education
Languages : en
Pages : 169
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Book Description
Discover high-impact leadership strategies for a thriving learning community! This compelling new book provides straightforward guidance and solutions for educators working to transform their school environments. Concrete examples of practical, high-impact, and evidence-based practices help you leverage the “big ideas” of Professional Learning Communities to promote lasting improvement in your school. You’ll learn to: Understand the essential role principals and teacher leaders play in leading PLCs Foster an understanding of how PLCs can support implementation of major instructional shifts such as the new Common Core State Standards Apply high-leverage strategies across your own school and district to improve instruction
Author: Darrin Speegle
Publisher: CRC Press
ISBN: 1000504166
Category : Business & Economics
Languages : en
Pages : 513
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Book Description
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested. The exercises in the book have been added to to the free and open online homework system myopenmath (https://www.myopenmath.com/) which may be useful to instructors.
Author: Paul Bambrick-Santoyo
Publisher: John Wiley & Sons
ISBN: 0470548746
Category : Education
Languages : en
Pages : 298
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Book Description
Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD.
Author: Joe Bostian
Publisher: IBM Redbooks
ISBN: 073846113X
Category : Computers
Languages : en
Pages : 16
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Book Description
Open-source software (OSS) is widely available and serves as an essential component for enterprises in the artificial intelligence (AI) and machine learning (ML) industry. Specifically, the open-source programming language Python is one of the most versatile and popular programming languages that are used in the world at the time of writing. This situation is especially true in the data science community, where Python provides many libraries and tools that enable essential AI and ML functions, and where it is supported by a large community of developers that actively contribute to its development. Understanding and managing vulnerabilities within OSS can be complex because of the many components, dependencies, and contributors that are involved. Although the nature of OSS helps balance access to programming and technology, it also results in fast-paced changes to software, which emphasizes the importance of software currency to minimize security concerns. Enterprises understand the critical need to have access to and leverage reputable open-source projects with proper maintenance, updates, transparency, reliable support, and a sense of control to form a secure foundation for implementing AI solutions. Python AI Toolkit for IBM® z/OS® is a powerful set of tools and libraries that is used to establish a secure foundation for AI development and deployment on z/OS so that enterprises can leverage their existing infrastructure for these mission-critical applications. The OSS that is provided within Python AI Toolkit for IBM z/OS is scanned and vetted for security vulnerabilities so that users can make informed decisions when leveraging these Python packages. Packages can be installed and managed by using the Package Installer for Python (pip), which is a common Python package manager, enabling a familiar, flexible, and agile delivery experience while empowering developers to build AI solutions.
