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See below for a selection of the latest books from Databases category. Presented with a red border are the Databases books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Databases books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Business analytics is the application of statistical and quantitative analysis, as well as formal modeling, to decision making. This book examines under what circumstances and with which techniques one can reasonably infer cause and effect in a business setting and use the insight to drive business decisions. The book is rooted in realistic and important cases used to illustrate the importance of thinking clearly about causality and applying the techniques of business analytics.
This book provides a summary of the most recent developments in the research of both univariate and multivariate change point problems in the past 20 years. The emphasis is on multivariate process control and process diagnosis. The authors provide the fundamental statistical models and algorithms as well as practical solutions to applications in the manufacturing and healthcare systems. This book can be used as a reference book for anyone who is interested in statistical process control techniques, or a supplementary textbook for graduate-level courses such as Statistical Quality Control, Statistical Modeling of Manufacturing Systems, or Multivariate Statistics.
Big data security enables products like Hadoop to provide new knowledge in a secure fashion. Big data brings together many previously separate units of data that were partially secured by their host's technical inability to share that data seamlessly. The potential for data breach in a big data solution is huge. New processes and technology solutions are needed to enable sensitive data to be brought together safely allowing differential privacy, while simultaneously providing new knowledge from the aggregated results. This practical book shows how to protect Hadoop data from unauthorized access, and limit the ability of an attacker to corrupt or modify data in the event of a security breach.
Although the terms data mining and knowledge discovery and data mining (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to identify previously unknown patterns. Knowledge Discovery Process and Methods to Enhance Organizational Performance explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. Sharing the insights of international KDDM experts, it details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. The book supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process. It discusses the implications of data mining including security, privacy, ethical and legal considerations. Provides an introduction to KDDM, including the various models adopted in academia and industry Details critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects Proposes the use of hybrid approaches that couple data mining with other analytic techniques (e.g., data envelopment analysis, cluster analysis, and neural networks) to derive greater value and utility Demonstrates the applicability of the KDDM process beyond analytics Shares experiences of implementing and applying various stages of the KDDM process in organizations The book includes case study examples of KDDM applications in business and government. After reading this book, you will understand the critical success factors required to develop robust data mining objectives that are in alignment with your organization's strategic business objectives.
Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to the type of problems. For instance, distributed memory systems are considered ill-suited for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed overview of big data systems. The book adopts a challenge-centric approach in which platforms are evaluated based on their capabilities to solve specific challenges.
Learn how to build a serverless real-world application in the cloud that's reliable, secure, maintainable, and can handle millions of users. If you have experience building traditional web applications, this practical guide shows you how to get started with serverless. Cloud engineer Wietse Venema takes you through the steps necessary to build serverless applications with Cloud Run, a container-based serverless platform on Google Cloud. Through the course of the book, you'll learn how to become productive with serverless technology. You will build and explore several example applications that highlight different parts of the serverless stack, using (light) frontend technology and Go on the back end. You can also follow the lessons in the book using your own project on Google Cloud Platform. You'll learn how to: Build a serverless application with Google's Cloud Run and Firestore Approach testing and development Handle user management and authentication Combine serverless with a traditional relational database Run and monitor production services Integrate your application with external APIs
This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation. Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, but no specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
This book covers IoT and Big Data from a technical and business point of view. The book explains the design principles, algorithms, technical knowledge, and marketing for IoT systems. It emphasizes applications of big data and IoT. It includes scientific algorithms and key techniques for fusion of both areas. Real case applications from different industries are offering to facilitate ease of understanding the approach. The book goes on to address the significance of security algorithms in combing IoT and big data which is currently evolving in communication technologies. The book is written for researchers, professionals, and academicians from interdisciplinary and transdisciplinary areas. The readers will get an opportunity to know the conceptual ideas with step-by-step pragmatic examples which makes ease of understanding no matter the level of the reader.
The twenty-first century is a time of intensifying competition and progressive digitization. Individual employees, managers, and entire organizations are under increasing pressure to succeed. The questions facing us today are: What does success mean? Is success a matter of chance and luck or perhaps is success a category that can be planned and properly supported? Business Intelligence and Big Data: Drivers of Organizational Success examines how the success of an organization largely depends on the ability to anticipate and quickly respond to challenges from the market, customers, and other stakeholders. Success is also associated with the potential to process and analyze a variety of information and the means to use modern information and communication technologies (ICTs). Success also requires creative behaviors and organizational cleverness from an organization. The book discusses business intelligence (BI) and Big Data (BD) issues in the context of modern management paradigms and organizational success. It presents a theoretically and empirically grounded investigation into BI and BD application in organizations and examines such issues as: Analysis and interpretation of the essence of BI and BD Decision support Potential areas of BI and BD utilization in organizations Factors determining success with using BI and BD The role of BI and BD in value creation for organizations Identifying barriers and constraints related to BI and BD design and implementation The book presents arguments and evidence confirming that BI and BD may be a trigger for making more effective decisions, improving business processes and business performance, and creating new business. The book proposes a comprehensive framework on how to design and use BI and BD to provide organizational success.
A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.