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Data Analytics
Data analytics is creeping into the lexicon of our daily language. This book gives the reader a perspective as to the overall data analytics skill set, startingwith a primer on statistics, and works toward the application of those methods.There are a variety of formulas and algorithms used in the data analyticsprocess. These formulas can be plugged into whatever software application thereader uses to obtain the answer they need. There are several demonstrations ofthis process to provide straightforward instruction as to how to bring data analytics skills to your critical thinking. This bookpresents a variety of methods and techniques, as well as case studies, toenrich the knowledge of data analytics for project managers, systems engineers,and cybersecurity professionals. It separates the case studies so that eachprofession can practice some straightforward data analytics specific to their fields. The main purpose of this text is to refresh the statistical knowledgenecessary to build models for data analytics. Along with that, this bookencompasses the analytics thinking that is essential to all three professions.FEATURES:Provides straightforward instruction on data analytics methodsIncludes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionalsRefreshes the statistical knowledgeneeded to bring data analytics into your skillset and decision-makingFocuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking
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Data Analytics
Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.
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Data Analytics
This book constitutes the refereed conference proceedings of the 31st British International Conference on Databases, BICOD 2017 - formerly known as BNCOD (British National Conference on Databases) - held in London, UK, in July 2017.The 17 revised full papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as data cleansing, data integration, data wrangling, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data provenance and ontology-based data access. They are organized in the following topical sections: data wrangling and data integration; data analysis and data mining; graph data querying and analysis; multidimensional data and data quality; and distributed and multimedia data management.
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Fundamentals of Data Analytics
This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
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Data Analytics
Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and techniques for data analytics and to serve as a quick reference to keep pertinent formulas within fingertip reach of readers. This handbook includes formulas that will appeal to mathematically inclined readers. It discusses how to use data analytics to improve decision-making and is ideal for those new to using data analytics to show how to expand their usage horizon. It provides quantitative techniques for modeling pandemics, such as COVID-19. It also adds to the suite of mathematical tools for emerging technical areas. This handbook is a handy reference for researchers, practitioners, educators, and students in areas such as industrial engineering, production engineering, project management, civil engineering, mechanical engineering, technology management, and business management worldwide.
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Fundamentals of Data Analytics
This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
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R 1,008.00
Data Analytics
If you are a manager who receives the results of any data analyst’s work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute.Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics.The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.
R 2,820.20
Hands-on Data Analytics for Business
The knowledge and skills of data analytics enable business professionals to make evidence-based decisions rather than just by intuition. To appreciate the knowledge and skills, one needs to understand the theory, hands-on statistical operations using computer software, business application case studies and the analytics project maturity roadmap.Hands-on Data Analytics for Business is a concise all-in-one analytics textbook for business professionals and university students who want to:Understand the essential theories and concepts of data analyticsGrasp hands-on practical statistical and analytics skills step-by-step using SPSS and ExcelAppreciate real-world well-known case studies in human resources, marketing and supply chain on how data analytics is applied in business.This book is for business professionals or university students seeking to master data analytics knowledge and technical skills to a level allowing them to practice data analytics hands-on and manage data analytics projects at scale. For business professionals, the book is written in a way to help learn statistical skills in a concise way with practical real-world examples to inspire business applications. For students, this book is organized in a sequence readily deliverable for a university course, with classes combining theory or concepts, hands-on practice and case studies. For all readers, the book is an essential guide on using data analytics to tackle modern complex activities for companies to gain a competitive edge.
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Analytics: Data Science, Data Analysis and Predictive Analytics for Business
SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. Don't you wish to have the power to know what your target consumers are thinking? Won't you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time.
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Data Warehousing and Analytics
This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
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Data-Enabled Analytics
This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
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Data Analytics for IT Networks
Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data
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Hands-on Data Analytics for Business
The knowledge and skills of data analytics enable business professionals to make evidence-based decisions rather than just by intuition. To appreciate the knowledge and skills, one needs to understand the theory, hands-on statistical operations using computer software, business application case studies and the analytics project maturity roadmap.Hands-on Data Analytics for Business is a concise all-in-one analytics textbook for business professionals and university students who want to:Understand the essential theories and concepts of data analyticsGrasp hands-on practical statistical and analytics skills step-by-step using SPSS and ExcelAppreciate real-world well-known case studies in human resources, marketing and supply chain on how data analytics is applied in business.This book is for business professionals or university students seeking to master data analytics knowledge and technical skills to a level allowing them to practice data analytics hands-on and manage data analytics projects at scale. For business professionals, the book is written in a way to help learn statistical skills in a concise way with practical real-world examples to inspire business applications. For students, this book is organized in a sequence readily deliverable for a university course, with classes combining theory or concepts, hands-on practice and case studies. For all readers, the book is an essential guide on using data analytics to tackle modern complex activities for companies to gain a competitive edge.
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Data-Enabled Analytics
This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
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General Introduction to Data Analytics
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniquesA unique illustrative example that extends throughout all the chaptersExercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
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Data Analytics Advantage
The Data Analytics Advantage is a comprehensive guide that equips readers with the essential skills to analyze and leverage social media data for strategic decision-making. Combining academic principles with case studies, social media analytics expert Laeeq Khan provides a foundational understanding of the field and its importance, introduces cutting-edge trends and tools through interactive tutorials and exercises, and offers a glimpse into future developments, including the potential impact of artificial intelligence.With its balance of technical rigor and accessibility, the textbook requires minimal coding experience while covering a wide range of topics. Using a three-stage framework--discovery, analysis, visualization--Khan emphasizes the importance of question formulation, data collection, and the skillful application of analytical tools; unveils key aspects of social media analytics, from hashtag and temporal analysis to social network analysis, data visualization, and storytelling; introduces readers to various methodologies, including monitoring metrics and KPIs, adopting advanced data visualization techniques, and mastering sentiment and network analysis; and addresses potential challenges in social media analytics, such as data access, privacy, and ethics. Readers will come away with a solid grasp of theoretical concepts as well as hands-on experience through practical exercises, making The Data Analytics Advantage an indispensable resource for students, social media administrators, marketers, and data analysts alike in the rapidly evolving field of social media analytics.
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Be Data Analytical
Be Data Analytical is the book organizations and individuals need to understand how to truly use analytics to turn data into valuable insights and drive smarter decision making.Data needs analytics to turn it into value and for organizations to be truly data-driven, they need to use analytics correctly. However, most organizations do not move beyond the first, most rudimentary stage of analytics. They miss out on the powerful insights and opportunities available with all the four levels of analytics: descriptive, diagnostic, predictive and prescriptive. Be Data Analytical reveals how to supercharge data value through all the four levels of analytics, bringing data to life and enhancing data-driven decision making. Be Data Analytical examines each of these four levels of analytics in-depth: what they are, why they matter, how they can be used strategically and how they can be implemented. The book also explores how individuals and organizations can improve their skills and performance in each of these areas. Written by a global trailblazer in the world of data literacy, the book shows professionals, managers, leaders and organizations how to use analytics for the successful and strategic conversion of data into value, insight and action.
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Big Data Analytics
This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
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Data Analytics and Digital Transformation
Understanding the significance of data analytics is paramount for digital transformation but in many organisations they are separate units without fully aligned goals. As organisations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role for their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This creates new opportunities but also requires well considered and continuously assessed decision making as competitiveness is at stake. This book details approaches, concepts and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organisations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organisational studies and strategic management. It is also relevant for policy makers, regulators and executives of private and public organisations looking to implement successful transformation policies.
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Data Analytics in Finance
Data Analytics in Finance covers the methods and application of data analytics in all major areas of finance, including buy-side investments, sell-side investment banking, corporate finance, consumer finance, financial services, real estate, insurance, and commercial banking. It explains statistical inference of big data, financial modeling, machine learning, database querying, data engineering, data visualization, and risk analysis. Emphasizing financial data analytics practices with a solution- oriented purpose, it is a “one-stop-shop” of all the major data analytics aspects for each major finance area.The book paints a comprehensive picture of the data analytics process including:Statistical inference of big dataFinancial modelingMachine learning and AIDatabase queryingData engineeringData visualizationRisk analysisEach chapter is crafted to provide complete guidance for many subject areas including investments, fraud detection, and consumption finance. Avoiding data analytics methods widely available elsewhere, the book focuses on providing data analytics methods specifically applied to key areas of finance. Written as a roadmap for researchers, practitioners, and students to master data analytics instruments in finance, the book also provides a collection of indispensable resources for the readers’ reference. Offering the knowledge and tools necessary to thrive in a data-driven financial landscape, this book enables readers to deepen their understanding of investments, develop new approaches to risk management, and apply data analytics to finance.
R 4,026.42
