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Data Journeys in the Sciences
by Sabina Leonelli Niccolò TempiniThis groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.
Data Mining and Exploration: From Traditional Statistics to Modern Data Science
by Chong Ho Alex YuThis book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Data Mining for Bioinformatics
by Sumeet Dua Pradeep ChowriappaCovering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to he
Data Mining for Global Trends in Mountain Biodiversity
by Eva M. Spehn Christian KörnerThanks to advances in electronic archiving of biodiversity data and the digitization of climate and other geophysical data, a new era in biogeography, functional ecology, and evolutionary ecology has begun. In Data Mining for Global Trends in Mountain Biodiversity, Christian Korner, Eva M. Spehn, and a team of experts from the Global Mountain Biodi
Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies
by Christopher WestphalIn 2004, the Government Accountability Office provided a report detailing approximately 200 government-based data-mining projects. While there is comfort in knowing that there are many effective systems, that comfort isn‘t worth much unless we can determine that these systems are being effectively and responsibly employed.Written by one of the most
Data Mining for Systems Biology: Methods and Protocols (Methods in Molecular Biology #939)
by Charles Delisi Minoru Kanehisa Hiroshi MamitsukaThe post-genomic revolution is witnessing the generation of petabytes of data annually, with deep implications ranging across evolutionary theory, developmental biology, agriculture, and disease processes. Data Mining for Systems Biology: Methods and Protocols, surveys and demonstrates the science and technology of converting an unprecedented data deluge to new knowledge and biological insight. The volume is organized around two overlapping themes, network inference and functional inference. Written in the highly successful Methods in Molecular BiologyTM series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols also seeks to aid researchers in the further development of databases, mining and visualization systems that are central to the paradigm altering discoveries being made with increasing frequency.
Data Mining for Systems Biology: Methods and Protocols (Methods in Molecular Biology #1807)
by Hiroshi MamitsukaThis fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.
Data Mining in Biomedical Imaging, Signaling, and Systems
by Rajendra Acharya U Sumeet DuaData mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedi
Data Mining in Clinical Medicine (Methods in Molecular Biology #1246)
by Carlos Fernández-Llatas Juan Miguel García-GómezThis volume complies a set of Data Mining techniques and new applications in real biomedical scenarios. Chapters focus on innovative data mining techniques, biomedical datasets and streams analysis, and real applications. Written in the highly successful Methods in Molecular Biology series format, chapters are thought to show to Medical Doctors and Engineers the new trends and techniques that are being applied to Clinical Medicine with the arrival of new Information and Communication technologiesAuthoritative and practical, Data Mining in Clinical Medicine seeks to aid scientists with new approaches and trends in the field.
Data Mining in Crystallography (Structure and Bonding #134)
by D. W. Hofmann Liudmila N. KuleshovaAn Introduction to Data Mining. - Data Bases, the Basis for Data Mining. - Data Mining and Inorganic Crystallography. - Data Mining in Organic Crystallography. - Data Mining for Protein Secondary Structure Prediction.
Data Mining in Proteomics: From Standards to Applications (Methods in Molecular Biology #696)
by Christian Stephan Martin Eisenacher Michael HamacherThrough the rapid development of proteomics methods and technologies, an enormous amount of data was created, leading to a wide-spread rethinking of strategy design and data interpretation. In Data Mining in Proteomics: From Standards to Applications, experts in the field present these new insights within the proteomics community, taking the historical evolution as well as the most important international standardization projects into account. Along with basic and sophisticated overviews of proteomics technologies, standard data formats, and databases, the volume features chapters on data interpretation strategies including statistics, spectra interpretation, and analysis environments as well as specialized tasks such as data annotation, peak picking, phosphoproteomics, spectrum libraries, LC/MS imaging, and splice isoforms. As a part of the highly successful Methods in Molecular BiologyTM series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and cutting-edge, Data Mining in Proteomics: From Standards to Applications is a well-balanced compendium for beginners and experts, offering a broad scope of data mining topics but always focusing on the current state-of-the-art and beyond.
Data Mining in Structural Dynamic Analysis: A Signal Processing Perspective
by Magd Abdel Wahab Yun Lai Zhou Nuno M. M. Maia Linya Liu Elói FigueiredoThis book highlights the applications of data mining technologies in structural dynamic analysis, including structural design, optimization, parameter identification, model updating, damage identification, in civil, mechanical, and aerospace engineering. These engineering applications require precise structural design, fabrication, inspection, and further monitoring to obtain a full life-cycle analysis, and by focusing on data processing, data mining technologies offer another aspect in structural dynamic analysis. Discussing techniques in time/frequency domain, such as Hilbert transforms, wavelet theory, and machine learning for structural dynamic analysis to help in structural monitoring and diagnosis, the book is an essential reference resource for beginners, graduates and industrial professionals in various fields.
Data Mining Techniques for the Life Sciences (Methods in Molecular Biology #1415)
by Oliviero Carugo Frank EisenhaberWhereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the problem of interpreting accumulated data in terms of biological function and biomolecular mechanisms. In "Data Mining Techniques for the Life Sciences", experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. Beginning with a section covering the concepts and structures of important groups of databases for biomolecular mechanism research, the book then continues with sections on formal methods for analyzing biomolecular data and reviews of concepts for analyzing biomolecular sequence data in context with other experimental results that can be mapped onto genomes. As a volume of the highly successful Methods in Molecular BiologyTM series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and easy to reference, "Data Mining Techniques for the Life Sciences" seeks to aid students and researchers in the life sciences who wish to get a condensed introduction into the vital world of biological databases and their many applications.
Data Mining Techniques for the Life Sciences (Methods in Molecular Biology #2449)
by Oliviero Carugo Frank EisenhaberThis third edition details new and updated methods and protocols on important databases and data mining tools. Chapters guides readers through archives of macromolecular sequences and three-dimensional structures, databases of protein-protein interactions, methods for prediction conformational disorder, mutant thermodynamic stability, aggregation, and drug response. Quality of structural data and their release, soft mechanics applications in biology, and protein flexibility are considered, too, together with pan-genome analyses, rational drug combination screening and Omics Deep Mining. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials, includes step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Third Edition aims to be a practical guide to researches to help further their study in this field.
Data, Models and Analysis: The Highest Impact Articles in 'Atmosphere-Ocean'
by Guoqi Han, Hai Lin and Douw SteynThis volume contains the ten most cited articles that have appeared in the journal Atmosphere-Ocean since 1995. These articles cover a wide range of topics in meteorology, climatology and oceanography. Modelling work is represented in five papers, covering global climate model development; a cumulus parameterization scheme for global climate models; development of a regional forecast modelling system and parameterization of peatland hydraulic processes for climate models. Data rehabilitation and compilation in order to support trend analysis work on comprehensive precipitation and temperature data sets is presented in four papers. Field studies are represented by a paper on the circumpolar lead system. While the modelling studies are global in their application and applicability, the data analysis and field study papers cover environments that are specifically, but not uniquely, Canadian. This book will be of interest to researchers, students and professionals in the various sub-fields of meteorology, oceanography and climate science.
Data Monitoring Committees (DMCs): Past, Present, and Future
by Nand Kishore Rawat David KerrThis book provides an overview of Data Monitoring Committees(DMC) - what was done in the past, what is currently being done, and thoughts on improvements for the future. Previous works focused primarily on large cardiovascular studies (where DMCs originated more than 30 years ago) but updated references are needed that discuss smaller, more flexible studies in areas such as oncology. The authors have attended ~800 DMC meetings from ~200 distinct studies across all areas of clinical studies (oncology, rheumatology, rare diseases, cardiology, immunology, etc.) This wide range of expertise will be used, as well as the expertise that comes from working with virtually every large biotechnology and pharmaceutical company and CRO for DMC work. The reader of the book will know when DMCs are needed or helpful, how to form the DMC, how to work with external CROs and with sponsor teams and the DMC to create needed DMC outputs, how the DMC meetings are conducted, and - especially for DMC members - what are considerations within the Closed Session to review safety/efficacy outputs to assess risk/benefit to make appropriate recommendations that protect the patient safety and trial integrity. This is a practical hands-on book on how to decide if a DMC is necessary, how to form the DMC, how to expertly create the necessary materials for the DMC and have smooth running DMC meetings. There is no specialized training in school about how DMCs work - frequently people may have been in industry for many years without ever needing to work with a DMC. This book is the helpful reference for those new to these DMCs. The DMC work is critical to be correctly implemented as the impact of DMC activity on safeguarding the trial is so important.This book provides the following: Provides thorough instructions on the steps needed to form and implement a Data Monitoring Committee for clinical trial evaluation;Includes practical and hands-on information on DMC implementation;Discusses a wide range of clinical trial – by phase and therapeutic area.
Data Production and Analysis in Population Genomics: Methods and Protocols (Methods in Molecular Biology #888)
by Aurélie Bonin François PompanonPopulation genomics is a recently emerged discipline, which aims at understanding how evolutionary processes influence genetic variation across genomes. Today, in the era of cheaper next-generation sequencing, it is no longer as daunting to obtain whole genome data for any species of interest and population genomics is now conceivable in a wide range of fields, from medicine and pharmacology to ecology and evolutionary biology. However, because of the lack of reference genome and of enough a priori data on the polymorphism, population genomics analyses of populations will still involve higher constraints for researchers working on non-model organisms, as regards the choice of the genotyping/sequencing technique or that of the analysis methods. Therefore, Data Production and Analysis in Population Genomics purposely puts emphasis on protocols and methods that are applicable to species where genomic resources are still scarce. It is divided into three convenient sections, each one tackling one of the main challenges facing scientists setting up a population genomics study. The first section helps devising a sampling and/or experimental design suitable to address the biological question of interest. The second section addresses how to implement the best genotyping or sequencing method to obtain the required data given the time and cost constraints as well as the other genetic resources already available, Finally, the last section is about making the most of the (generally huge) dataset produced by using appropriate analysis methods in order to reach a biologically relevant conclusion. Written in the successful Methods in Molecular BiologyTM series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, advice on methodology and implementation, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Data Production and Analysis in Population Genomics serves a wide readership by providing guidelines to help choose and implement the best experimental or analytical strategy for a given purpose.
Data Professionals at Work
by Malathi MahadevanEnjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up.You’ll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking to finance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields.Interviewees in the book include: Mindy Curnutt, Julie Smith, Kenneth Fisher, Andy Leonard, Jes Borland, Kevin Feasel, Ginger Grant, Vicky Harp, Kendra Little, Jason Brimhall, Tim Costello, Andy Mallon, Steph Locke, Jonathan Stewart, Joseph Sack, John Q. Martin, John Morehouse, Kathi Kellenberger, Argenis Fernandez, Kirsten Benzel, Tracy Boggiano, Dave Walden, Matt Gordon, Jimmy May, Drew Furgiuele, Marlon Ribunal, and Joseph Fleming. All of them have been successful in their careers, and share their perspectives on working and succeeding in the field as data and database professionals. What You'll LearnStand out as an outstanding professional in your area of data work by developing the right set of skills and attitudes that lead to successAvoid common mistakes and pitfalls, and recover from operational failures and bad technology decisionsUnderstand current trends and best practices, and stay out in front as the field evolvesBreak into working with data through database administration, business intelligence, or any of the other career paths represented in this bookManage stress and develop a healthy work-life balance no matter which career path you decide uponChoose a suitable path for yourself from among the different career paths in working with dataWho This Book Is ForDatabase administrators and developers, database and business intelligence architects, consultants, and analytic professionals, as well as those intent on moving into one of those career paths. Aspiring data professionals and those in related technical fields who want to make a move toward managing or analyzing data on a full-time basis will find the book useful. Existing data professionals who want to be outstanding and successful at what they do will also appreciate the book's advice and guidance.
Data Protection for Software Development and IT: A Practical Introduction
by Ralf KneuperThis book introduces data protection, i.e. the protection of individuals from misuse of their personal data. It provides a special focus on the direct impact on software development, e.g. in the form of functional requirements for software systems resulting from data protection. Chapter 1 provides an initial overview of the basic concepts of data protection and its legal foundations. Chapter 2 then delves deeper into the European General Data Protection Regulation (GDPR), covering in particular its basic concepts, terminology and requirements. Next, the specific implementation and interpretation of GDPR requirements in software and IT are dealt with, starting in chapter 3 with the principles of data protection defined in GDPR and the rights of data subjects in chapter 4. Chapter 5 discusses data transfer between organizations, including the relevant constellations (e.g. through various service providers), the legal framework and its practical implementation. Subsequently, chapter 6 changes the view from individual regulations and their implementation to technical and organizational design of data protection, including its embedding in the software life cycle, while chapter 7 provides an overview of information security and its aspects relevant to data protection. Eventually, chapter 8 deals with data protection for organizations as they are data subjects themselves. The appendices contain the most important excerpts from the Charter of Fundamental Rights of the EU and GDPR in this context, a collection of links to relevant laws and supervisory authorities, as well as a glossary of the most important terms used. The book’s target groups include software developers, IT consultants, requirements analysts, IT operations personnel and project managers in IT projects, but also data protection managers and data protection officers in the context of software development and IT.
Data Protection in the Internet (Ius Comparatum - Global Studies in Comparative Law #38)
by Dário Moura Vicente Sofia de Vasconcelos CasimiroThis book identifies and explains the different national approaches to data protection – the legal regulation of the collection, storage, transmission and use of information concerning identified or identifiable individuals – and determines the extent to which they could be harmonised in the foreseeable future. In recent years, data protection has become a major concern in many countries, as well as at supranational and international levels. In fact, the emergence of computing technologies that allow lower-cost processing of increasing amounts of information, associated with the advent and exponential use of the Internet and other communication networks and the widespread liberalization of the trans-border flow of information have enabled the large-scale collection and processing of personal data, not only for scientific or commercial uses, but also for political uses. A growing number of governmental and private organizations now possess and use data processing in order to determine, predict and influence individual behavior in all fields of human activity. This inevitably entails new risks, from the perspective of individual privacy, but also other fundamental rights, such as the right not to be discriminated against, fair competition between commercial enterprises and the proper functioning of democratic institutions. These phenomena have not been ignored from a legal point of view: at the national, supranational and international levels, an increasing number of regulatory instruments – including the European Union’s General Data Protection Regulation applicable as of 25 May 2018 – have been adopted with the purpose of preventing personal data misuse. Nevertheless, distinct national approaches still prevail in this domain, notably those that separate the comprehensive and detailed protective rules adopted in Europe since the 1995 Directive on the processing of personal data from the more fragmented and liberal attitude of American courts and legislators in this respect. In a globalized world, in which personal data can instantly circulate and be used simultaneously in communications networks that are ubiquitous by nature, these different national and regional approaches are a major source of legal conflict.
Data Requirements for Integrated Urban Water Management: Urban Water Series - UNESCO-IHP (Urban Water Series)
by Tim D. Fletcher and Ana DeletiicIntegrated urban water management relies on data allowing us to analyse, understand and predict the behaviour of the individual water cycle components and their interactions. The concomitant monitoring of the complex of urban water system elements makes it possible to grasp the entirety of relations among the various components of the urban water c
Data Science and Big Data Analytics in Smart Environments
by Marta Chinnici; Florin Pop; Cătălin NegruMost applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment.Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
Data Science and Human-Environment Systems
by Steven M. MansonTransformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.
Data Science and SDGs: Challenges, Opportunities and Realities
by Bikas Kumar Sinha Md. Nurul Haque MollahThe book presents contributions on statistical models and methods applied, for both data science and SDGs, in one place. Measuring and controlling data of SDGs, data driven measurement of progress needs to be distributed to stakeholders. In this situation, the techniques used in data science, specially, in the big data analytics, play an important role rather than the traditional data gathering and manipulation techniques. This book fills this space through its twenty contributions. The contributions have been selected from those presented during the 7th International Conference on Data Science and Sustainable Development Goals organized by the Department of Statistics, University of Rajshahi, Bangladesh; and cover topics mainly on SDGs, bioinformatics, public health, medical informatics, environmental statistics, data science and machine learning. The contents of the volume would be useful to policymakers, researchers, government entities, civil society, and nonprofit organizations for monitoring and accelerating the progress of SDGs.
Data Science Careers, Training, and Hiring: A Comprehensive Guide to the Data Ecosystem: How to Build a Successful Data Science Career, Program, or Unit (SpringerBriefs in Computer Science)
by Renata Rawlings-GossThis book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.