| bs data management and analytics: Data Management, Analytics and Innovation Neha Sharma, Amlan Chakrabarti, Valentina Emilia Balas, 2019-10-24 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| bs data management and analytics: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| bs data management and analytics: A Practitioner's Guide to Business Analytics (PB) Randy Bartlett, 2013-01-25 Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice. |
| bs data management and analytics: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| bs data management and analytics: Business Analytics S. Christian Albright, Wayne L. Winston, 2017 |
| bs data management and analytics: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics |
| bs data management and analytics: Business Analytics with Management Science Models and Methods Arben Asllani, 2015 This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems. |
| bs data management and analytics: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| bs data management and analytics: Big Data Analytics Kim H. Pries, Robert Dunnigan, 2015-02-05 With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif |
| bs data management and analytics: Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal, 2015-06-23 At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available |
| bs data management and analytics: Big Data Management And Analytics Brij B Gupta, Mamta, 2023-12-05 With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance. |
| bs data management and analytics: Data Management, Analytics and Innovation Valentina Emilia Balas, Neha Sharma, Amlan Chakrabarti, 2018-09-07 The volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry. |
| bs data management and analytics: The Applied Business Analytics Casebook Matthew J. Drake, 2014 The first collection of cases on big data analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker. Perfect for students, practitioners, and certification candidates in SCM, OM, and OR, these short, focused, to-the-point case studies illustrate the entire decision-making process. They provide realistic opportunities to perform analyses, interpret output, and recommend an optimal course of action. Contributed by leading big data experts, the cases in The Applied Business Analytics Casebook covers: Forecasting and statistical analysis: time series forecasting models, regression models, data visualization, and hypothesis testing Optimization and simulation: linear, integer, and nonlinear programming; Monte Carlo simulation and risk analysis; and stochastic optimization Decision analysis: decision making under uncertainty; expected value of perfect information; decision trees; game theory models; AHP; and multi-criteria decision making Advanced business analytics: data warehousing/mining; text mining; neural networks; financial analytics; CRM analytics; and revenue management models |
| bs data management and analytics: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| bs data management and analytics: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| bs data management and analytics: Data Management on New Hardware Spyros Blanas, Rajesh Bordawekar, Tirthankar Lahiri, Justin Levandoski, Andrew Pavlo, 2017-03-21 This book contains selected papers from the 7th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016, and the 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, held in New Dehli, India, in September 2016. The joint Workshops were co-located with VLDB 2016. The 9 papers presented were carefully reviewed and selected from 18 submissions. They investigate opportunities in accelerating analytics/data management systems and workloads (including traditional OLTP, data warehousing/OLAP, ETL streaming/real-time, business analytics, and XML/RDF processing) running memory-only environments, using processors (e.g. commodity and specialized multi-core, GPUs and FPGAs, storage systems (e.g. storage-class memories like SSDs and phase-change memory), and hybrid programming models like CUDA, OpenCL, and Open ACC. The papers also explore the interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modeling and evaluation, from the perspective of data management applications. |
| bs data management and analytics: Data Management, Analytics and Innovation Saptarsi Goswami, Inderjit Singh Barara, Amol Goje, C. Mohan, Alfred M. Bruckstein, 2022-09-21 This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Sixth International Conference on Data Management, Analytics and Innovation (ICDMAI 2022), held virtually during January 14–16, 2022. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. |
| bs data management and analytics: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| bs data management and analytics: Data Analytics and Visualization in Quality Analysis using Tableau Jaejin Hwang, Youngjin Yoon, 2021-07-28 Data Analytics and Visualization in Quality Analysis using Tableau goes beyond the existing quality statistical analysis. It helps quality practitioners perform effective quality control and analysis using Tableau, a user-friendly data analytics and visualization software. It begins with a basic introduction to quality analysis with Tableau including differentiating factors from other platforms. It is followed by a description of features and functions of quality analysis tools followed by step-by-step instructions on how to use Tableau. Further, quality analysis through Tableau based on open source data is explained based on five case studies. Lastly, it systematically describes the implementation of quality analysis through Tableau in an actual workplace via a dashboard example. Features: Describes a step-by-step method of Tableau to effectively apply data visualization techniques in quality analysis Focuses on a visualization approach for practical quality analysis Provides comprehensive coverage of quality analysis topics using state-of-the-art concepts and applications Illustrates pragmatic implementation methodology and instructions applicable to real-world and business cases Include examples of ready-to-use templates of customizable Tableau dashboards This book is aimed at professionals, graduate students and senior undergraduate students in industrial systems and quality engineering, process engineering, systems engineering, quality control, quality assurance and quality analysis. |
| bs data management and analytics: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| bs data management and analytics: The Accidental Data Scientist Amy L. Affelt, 2015 Amy Affelt, author of The Accidental Data Scientist, notes that Librarians and information professionals have always worked with data in order to meet the information needs of their constituents, thus 'Big Data' is not a new concept for them. With The Accidental Data Scientist, Amy Affelt shows information professionals how to leverage their skills and training to master emerging tools, techniques, and vocabulary; create mission-critical Big Data research deliverables; and discover rewarding new career opportunities by embracing their inner Data Scientist. |
| bs data management and analytics: A First Course in Machine Learning Simon Rogers, Mark Girolami, 2016-10-14 Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/ |
| bs data management and analytics: Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics Taniar, David, 2009-02-28 Provides developments and research, as well as current innovative activities in data warehousing and mining, focusing on the intersection of data warehousing and business intelligence. |
| bs data management and analytics: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| bs data management and analytics: IAPP CIPM Certified Information Privacy Manager Study Guide Mike Chapple, Joe Shelley, 2023-01-19 An essential resource for anyone preparing for the CIPM certification exam and a career in information privacy As cybersecurity and privacy become ever more important to the long-term viability and sustainability of enterprises in all sectors, employers and professionals are increasingly turning to IAPP’s trusted and recognized Certified Information Privacy Manager qualification as a tried-and-tested indicator of information privacy management expertise. In IAPP CIPM Certified Information Privacy Manager Study Guide, a team of dedicated IT and privacy management professionals delivers an intuitive roadmap to preparing for the CIPM certification exam and for a new career in the field of information privacy. Make use of pre-assessments, the Exam Essentials feature, and chapter review questions with detailed explanations to gauge your progress and determine where you’re proficient and where you need more practice. In the book, you’ll find coverage of every domain tested on the CIPM exam and those required to succeed in your first—or your next—role in a privacy-related position. You’ll learn to develop a privacy program and framework, as well as manage the full privacy program operational lifecycle, from assessing your organization’s needs to responding to threats and queries. The book also includes: A head-start to obtaining an in-demand certification used across the information privacy industry Access to essential information required to qualify for exciting new career opportunities for those with a CIPM credential Access to the online Sybex learning environment, complete with two additional practice tests, chapter review questions, an online glossary, and hundreds of electronic flashcards for efficient studying An essential blueprint for success on the CIPM certification exam, IAPP CIPM Certified Information Privacy Manager Study Guide will also ensure you hit the ground running on your first day at a new information privacy-related job. |
| bs data management and analytics: Handbook of Big Data Research Methods Shahriar Akter, Samuel Fosso Wamba, 2023-06-01 This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics. |
| bs data management and analytics: Information Quality and Governance for Business Intelligence Yeoh, William, 2013-12-31 Business intelligence initiatives have been dominating the technology priority list of many organizations. However, the lack of effective information quality and governance strategies and policies has been meeting these initiatives with some challenges. Information Quality and Governance for Business Intelligence presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth. This book is an essential reference tool for researchers, practitioners, and university students specializing in business intelligence, information quality, and information systems. |
| bs data management and analytics: CompTIA CySA+ Study Guide Mike Chapple, David Seidl, 2017-04-24 NOTE: The name of the exam has changed from CSA+ to CySA+. However, the CS0-001 exam objectives are exactly the same. After the book was printed with CSA+ in the title, CompTIA changed the name to CySA+. We have corrected the title to CySA+ in subsequent book printings, but earlier printings that were sold may still show CSA+ in the title. Please rest assured that the book content is 100% the same. Prepare yourself for the newest CompTIA certification The CompTIA Cybersecurity Analyst+ (CySA+) Study Guide provides 100% coverage of all exam objectives for the new CySA+ certification. The CySA+ certification validates a candidate's skills to configure and use threat detection tools, perform data analysis, identify vulnerabilities with a goal of securing and protecting organizations systems. Focus your review for the CySA+ with Sybex and benefit from real-world examples drawn from experts, hands-on labs, insight on how to create your own cybersecurity toolkit, and end-of-chapter review questions help you gauge your understanding each step of the way. You also gain access to the Sybex interactive learning environment that includes electronic flashcards, a searchable glossary, and hundreds of bonus practice questions. This study guide provides the guidance and knowledge you need to demonstrate your skill set in cybersecurity. Key exam topics include: Threat management Vulnerability management Cyber incident response Security architecture and toolsets |
| bs data management and analytics: Data-Driven Quality Improvement and Sustainability in Health Care Patricia L. Thomas, PhD, RN, FAAN, FNAP, FACHE, NEA-BC, ACNS-BC, CNL, James L. Harris, PhD, APRN-BC, MBA, CNL, FAAN, Brian J. Collins, BS, MA, 2020-11-19 Data-Driven Quality Improvement and Sustainability in Health Care: An Interprofessional Approach provides nurse leaders and healthcare administrators of all disciplines with a solid understanding of data and how to leverage data to improve outcomes, fuel innovation, and achieve sustained results. It sets the stage by examining the current state of the healthcare landscape; new imperatives to meet policy, regulatory, and consumer demands; and the role of data in administrative and clinical decision-making. It helps the professional identify the methods and tools that support thoughtful and thorough data analysis and offers practical application of data-driven processes that determine performance in healthcare operations, value- and performance-based contracts, and risk contracts. Misuse or inconsistent use of data leads to ineffective and errant decision-making. This text highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls. In addition, chapters feature key points, reflection questions, and real-life interprofessional case exemplars to help the professional draw distinctions and apply principles to their own practice. Key Features: Provides nurse leaders and other healthcare administrators with an understanding of the role of data in the current healthcare landscape and how to leverage data to drive innovative and sustainable change Offers frameworks, methodology, and tools to support quality improvement measures Demonstrates the application of data and how it shapes quality and safety initiatives through real-life case exemplars Highlights common barriers and pitfalls related to data use and provide strategies for how to avoid these pitfalls |
| bs data management and analytics: IAPP CIPP / US Certified Information Privacy Professional Study Guide Mike Chapple, Joe Shelley, 2021-06-02 Prepare for success on the IAPP CIPP/US exam and further your career in privacy with this effective study guide - now includes a downloadable supplement to get you up to date on the 2022 CIPP exam! Information privacy has become a critical and central concern for small and large businesses across the United States. At the same time, the demand for talented professionals able to navigate the increasingly complex web of legislation and regulation regarding privacy continues to increase. Written from the ground up to prepare you for the United States version of the Certified Information Privacy Professional (CIPP) exam, Sybex’s IAPP CIPP/US Certified Information Privacy Professional Study Guide also readies you for success in the rapidly growing privacy field. You’ll efficiently and effectively prepare for the exam with online practice tests and flashcards as well as a digital glossary. The concise and easy-to-follow instruction contained in the IAPP/CIPP Study Guide covers every aspect of the CIPP/US exam, including the legal environment, regulatory enforcement, information management, private sector data collection, law enforcement and national security, workplace privacy and state privacy law, and international privacy regulation. Provides the information you need to gain a unique and sought-after certification that allows you to fully understand the privacy framework in the US Fully updated to prepare you to advise organizations on the current legal limits of public and private sector data collection and use Includes access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms Perfect for anyone considering a career in privacy or preparing to tackle the challenging IAPP CIPP exam as the next step to advance an existing privacy role, the IAPP CIPP/US Certified Information Privacy Professional Study Guide offers you an invaluable head start for success on the exam and in your career as an in-demand privacy professional. |
| bs data management and analytics: Sport Analytics Gil Fried, Ceyda Mumcu, 2016-11-10 The increasing availability of data has transformed the way sports are played, promoted and managed. This is the first textbook to explain how the big data revolution is having a profound influence across the sport industry, demonstrating how sport managers and business professionals can use analytical techniques to improve their professional practice. While other sports analytics books have focused on player performance data, this book shows how analytics can be applied to every functional area of sport business, from marketing and event management to finance and legal services. Drawing on research that spans the entire sport industry, it explains how data is influencing the most important decisions, from ticket sales and human resources to risk management and facility operations. Each chapter contains real world examples, industry profiles and extended case studies which are complimented by a companion website full of useful learning resources. Sport Analytics: A data-driven approach to sport business and management is an essential text for all sport management students and an invaluable reference for any sport management professional involved in operational research. |
| bs data management and analytics: The Analytics Process Eduardo Rodriguez, 2017-02-17 This book is about the process of using analytics and the capabilities of analytics in today’s organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics’ real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied to other concepts, such as Big Data, are the be-all and end-all of the analytics process. They are, instead, only a step within a holistic and critical approach to management thinking that can create real value for an organization. To develop this holistic approach, the book is divided into two sections that examine concepts and applications. The first section makes the case for executive management taking a holistic approach to analytics. It draws on rich research in operations and management science that form the context in which analytics tools are to be applied. There is a strong emphasis on knowledge management concepts and techniques, as well as risk management concepts and techniques. The second section focuses on both the use of the analytics process and organizational issues that are required to make the analytics process relevant and impactful. |
| bs data management and analytics: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| bs data management and analytics: Demystifying Big Data and Machine Learning for Healthcare Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz, 2017-02-15 Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them. |
| bs data management and analytics: ICAS2014-International Conference on Analytics Driven Solutions Eduardo Rodriguez, Department of Leisure Studies Greg Richards, Greg Richards, 2014-09-10 |
| bs data management and analytics: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| bs data management and analytics: Fintech with Artificial Intelligence, Big Data, and Blockchain Paul Moon Sub Choi, Seth H. Huang, 2021-03-08 This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths. |
| bs data management and analytics: CompTIA Data+ Study Guide Mike Chapple, Sharif Nijim, 2022-03-18 Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guide CompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers. With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers: All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities 100% coverage of objectives for the NEW CompTIA Data+ exam Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms Ideal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start to beginning or accelerating a career as an in-demand data analyst. |
| bs data management and analytics: CompTIA DataSys+ Study Guide Mike Chapple, Sharif Nijim, 2023-10-12 Your all-in-one guide to preparing for the CompTIA DataSys+ exam In CompTIA DataSys+ Study Guide: Exam DS0-001, a team of accomplished IT experts delivers a practical and hands-on roadmap to succeeding on the challenging DS0-001 exam and in a new or existing career as a data systems professional. In the book, you’ll explore the essentials of databases, their deployment, management, maintenance, security, and more. Whether you’re preparing for your first attempt at the CompTIA DataSys+ exam or for your first day on the job at a new database-related IT position, this book walks you through the foundational and intermediate skills you need to have to succeed. It covers every objective tested by the DS0-001 and skills commonly required in the real-world. You’ll also find: Practice test questions that measure your readiness for the real exam and your ability to handle the challenges of a new data systems position Examples and scenarios drawn from real life, as well as challenging chapter review questions Complimentary access to Sybex’s interactive online learning environment and test bank, accessible from multiple devices, and including electronic flashcards and a searchable glossary Perfect for anyone getting ready to write the DS0-001 certification exam, CompTIA DataSys+ Study Guide: Exam DS0-001 is also an essential resource for everyone seeking the foundational knowledge and skills required to move into a database administrator role. |
| bs data management and analytics: Beyond Multi-Channel Marketing Maria Palazzo, Pantea Foroudi, Alfonso Siano, 2020-06-17 Delving into the rapidly developing field of dual marketing, investigating the strategic alliances, multi-stakeholder perspectives and branding potential it holds, this book promotes the adoption of the multichannel approach which is fundamental to facing the challenges of marketing 4.0. |
What Is the Difference Between a BA and a BS Degree?
May 30, 2025 · Learn more about the difference between these two bachelor's degrees and how to choose the best degree for your goals. The Bachelor of Arts (BA) and the Bachelor of Science …
Bachelor of Science (BS) Degree: Areas of Study, Careers, and More
May 30, 2025 · A Bachelor of Science (BS) is a type of bachelor's degree you can earn in certain majors, such as the natural sciences, mathematics, technology, engineering, and health. BS …
Bachelor of Science - Wikipedia
A Bachelor of Science (BS, BSc, B.S., B.Sc., SB, or ScB; from the Latin scientiae baccalaureus) [1] is a bachelor's degree that is awarded for programs that generally last three to five years. [2] The …
What Is a Bachelor’s Degree? Requirements, Costs, and More
May 30, 2025 · Bachelor of Science (BS): You earn a Bachelor of Science when you study technology, math, or one of the natural sciences, such as biology, chemistry, finance, or …
What Is a BS Degree? Is It Right for You? - PrepScholar
In this guide, we explain the BS degree meaning, subjects and skills BS students learn in college, popular BS degrees to get, how this degree type differs from other degrees like BA and BFA, and …
BA Degree vs. BS Degree: What’s the Difference and Which Is Better?
Nov 4, 2024 · The BA degree vs. BS degree choice comes down to whether you want a broad, flexible program (BA) or a focused, technical one (BS). So, in simple terms, a BA gives you …
Bachelor's Degrees | BA, BS, BBA, BPS Degrees | CollegeAtlas
Jun 24, 2014 · What is a bachelor’s degree? A bachelor’s degree, also called a baccalaureate degree, is an undergraduate degree offered by four-year colleges and universities. It requires the …
What is the Difference Between a BS, BA, BFA, and BAS Degree?
The difference between a BA and BS program is subtle, but generally a BA program focuses more on tactical and general application of the subject while a BS program focuses more on the …
What is a BS degree? - edX
Mar 18, 2025 · What is a BS degree, and why is it important? A bachelor of science degree program takes about four years to obtain and generally covers the basic information you need for a career …
Bas Vs Bs Degree (Pros & Cons Explained)
Feb 14, 2024 · BAs focus on humanities and liberal arts with flexibility, while BS degrees emphasize science and technical subjects with more specialization. Additionally, BAS degrees are career …
What Is the Difference Between a BA and a BS Degree?
May 30, 2025 · Learn more about the difference between these two bachelor's degrees and how to choose the best degree for your goals. The Bachelor of Arts (BA) and the Bachelor of Science …
Bachelor of Science (BS) Degree: Areas of Study, Careers, and More
May 30, 2025 · A Bachelor of Science (BS) is a type of bachelor's degree you can earn in certain majors, such as the natural sciences, mathematics, technology, engineering, and health. BS …
Bachelor of Science - Wikipedia
A Bachelor of Science (BS, BSc, B.S., B.Sc., SB, or ScB; from the Latin scientiae baccalaureus) [1] is a bachelor's degree that is awarded for programs that generally last three to five years. [2] The …
What Is a Bachelor’s Degree? Requirements, Costs, and More
May 30, 2025 · Bachelor of Science (BS): You earn a Bachelor of Science when you study technology, math, or one of the natural sciences, such as biology, chemistry, finance, or …
What Is a BS Degree? Is It Right for You? - PrepScholar
In this guide, we explain the BS degree meaning, subjects and skills BS students learn in college, popular BS degrees to get, how this degree type differs from other degrees like BA and BFA, and …
BA Degree vs. BS Degree: What’s the Difference and Which Is Better?
Nov 4, 2024 · The BA degree vs. BS degree choice comes down to whether you want a broad, flexible program (BA) or a focused, technical one (BS). So, in simple terms, a BA gives you …
Bachelor's Degrees | BA, BS, BBA, BPS Degrees | CollegeAtlas
Jun 24, 2014 · What is a bachelor’s degree? A bachelor’s degree, also called a baccalaureate degree, is an undergraduate degree offered by four-year colleges and universities. It requires the …
What is the Difference Between a BS, BA, BFA, and BAS Degree?
The difference between a BA and BS program is subtle, but generally a BA program focuses more on tactical and general application of the subject while a BS program focuses more on the …
What is a BS degree? - edX
Mar 18, 2025 · What is a BS degree, and why is it important? A bachelor of science degree program takes about four years to obtain and generally covers the basic information you need for a career …
Bas Vs Bs Degree (Pros & Cons Explained)
Feb 14, 2024 · BAs focus on humanities and liberal arts with flexibility, while BS degrees emphasize science and technical subjects with more specialization. Additionally, BAS degrees are career …
