New Books
Strategic Blueprint for Enterprise Analytics: Integrating Advanced Analytics into Data-Driven Business
This book is a comprehensive guide for professionals, leaders, and academics seeking to unlock the power of data and analytics in the modern business landscape. It delves deeply into the strategic, architectural, and managerial aspects of implementing enterprise analytics (EA) systems in large enterprises. The book is meticulously structured into three parts. Part 1 lays the foundation for adaptable architecture in EA. Part 2 explores technical considerations: data, cloud platforms, and AI solutions. The final part focuses on strategy execution, investment, and risk management. Acting as a comprehensive guide, the book enables the creation of robust EA capabilities that foster growth, optimize operations, and keep pace with EA's dynamic world. Whether readers are leaders harnessing data's potential, practitioners navigating analytics, or academics exploring this evolving domain, this book provides insights and knowledge to guide readers toward a thriving, data-driven future.
Data Science: the Hard Parts: Techniques for Excelling at Data Science
This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline--machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).
The Music in the Data: Corpus Analysis, Music Analysis, and Tonal Traditions
Putting forward an extensive new argument for a humanities-based approach to big-data analysis, The Music in the Data shows how large datasets of music, or music corpora, can be productively integrated with the qualitative questions at the heart of music research. The author argues that as well as providing objective evidence, music corpora can themselves be treated as texts to be subjectively read and creatively interpreted, allowing new levels of understanding and insight into music traditions. Each chapter in this book asks how we define a core music-theory topic, such as style, harmony, meter, function, and musical key, and then approaches the topic through considering trends within large musical datasets, applying a combination of quantitative analysis and qualitative interpretation. Throughout, several basic techniques of data analysis are introduced and explained, with supporting materials available online. Connecting the empirical information from corpus analysis with theories of musical and textual meaning, and showing how each approach can enrich the other, this book provides a vital perspective for scholars and students in music theory, musicology, and all areas of music research.
Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity
A core principle of the welfare state is that everyone pays taxes or contributions in exchange for universal insurance against social risks such as sickness, old age, unemployment, and plain bad luck. This solidarity principle assumes that everyone is a member of a single national insurance pool, and it is commonly explained by poor and asymmetric information, which undermines markets and creates the perception that we are all in the same boat. Living in the midst of an information revolution, this is no longer a satisfactory approach. This book explores, theoretically and empirically, the consequences of 'big data' for the politics of social protection. Torben Iversen and Philipp Rehm argue that more and better data polarize preferences over public insurance and often segment social insurance into smaller, more homogenous, and less redistributive pools, using cases studies of health and unemployment insurance and statistical analyses of life insurance, credit markets, and public opinion.
Distrust: Big Data, Data-Torturing, and the Assault on Science
There is no doubt science is currently suffering from a credibility crisis. This thought-provoking book argues that, ironically, science's credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fuelled by the technological advances in Big Data and the development of ever-increasingly powerful computers. Using a wide range of entertaining examples, this fascinating book examines the impacts of society's growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community.
Ethical Data Science: Prediction in the Public Interest
Can data science truly serve the public interest? Data-driven analysis shapes many interpersonal, consumer, and cultural experiences yet scientific solutions to social problems routinely stumble. All too often, predictions remain solely a technocratic instrument that sets financial interests against service to humanity. Amidst a growing movement to use science for positive change, Anne L. Washington offers a solution-oriented approach to the ethical challenges of data science. Ethical Data Science empowers those striving to create predictive data technologies that benefit more people. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests influencing data science. Structured around a typical data science workflow, the book systematically outlines the potential for more nuanced approaches to transforming data into meaningful patterns. Drawing on arts and humanities methods, it encourages readers to think critically about the full human potential of data science step-by-step. Situating data science within multiple layers of effort exposes dependencies while also pinpointing opportunities for research ethics and policy interventions. This approachable process lays the foundation for broader conversations with a wide range of audiences. Practitioners, academics, students, policy makers, and legislators can all learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book proves the limits of predictive technology controlled by the few and calls for more inclusive data science.
Data Smart: Using Data Science to Transform Information into Insight
Want to jump into data science but don't know where to start? Let's be real, data science is presented as something mystical and unattainable without the most powerful software, hardware, and data expertise. Real data science isn't about technology. It's about how you approach the problem. In this updated edition of Data Smart: Using Data Science to Transform Information into Insight, award-winning data scientist and bestselling author Jordan Goldmeier shows you how to implement data science problems using Excel while exposing how things work behind the scenes. Data Smart is your field guide to building statistics, machine learning, and powerful artificial intelligence concepts right inside your spreadsheet. Inside you'll find: Four-color data visualizations that highlight and illustrate the concepts discussed in the book Tutorials explaining complicated data science using just Microsoft Excel How to take what you've learned and apply it to everyday problems at work and life Advice for using formulas, Power Query, and some of Excel's latest features to solve tough data problems Smart data science solutions for common business challenges Explanations of what algorithms do, how they work, and what you can tweak to take your Excel skills to the next level Data Smart is a must-read for students, analysts, and managers ready to become data science savvy and share their findings with the world.
Handbook of Big Data Research Methods
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. With contributions from leading academics and practitioners, the Handbook analyses how big data analytics can be used in different sectors, including detecting credit fraud in the financial sector, identifying potential diseases in health care, and increasing customer loyalty in the telecommunication sector. Chapters explore the use of artificial intelligence in accounting, the construction of successful data science ecosystems using the public cloud, and transformational models of personal data protection in the digital era. The Handbook also discusses the difficulties of adopting a data science platform and how the public cloud can aid companies in overcoming these challenges. Exploring how industries rely on predictive analytics to improve their decision-making, this Handbook will be essential reading for students and scholars in business analytics, economics, information systems, innovation and technology, and research methods. It will also benefit data analysts, economists, human resource managers, marketers, neuroscientists, and social science researchers.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Longlisted for the National Book Award | New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric. We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
The SAGE Encyclopedia of Theory in Science, Technology, Engineering, and Mathematics
Project Description: Theories are part and parcel of every human activity that involves knowing about the world and our place in it. In all areas of inquiry from the most commonplace to the most scholarly and esoteric, theorizing plays a fundamental role. The SAGE Encyclopedia of Theory in Science, Technology, Engineering, and Mathematics focuses on the ways that various STEM disciplines theorize about their subject matter. How is thinking about the subject organized? What methods are used in moving a novice in given field into the position of a competent student of that subject? Within the pages of this landmark work, readers will learn about the complex decisions that are made when framing a theory, what goes into constructing a powerful theory, why some theories change or fail, how STEM theories reflect socio-historical moments in time and how - at their best - they form the foundations for exploring and unlocking the mysteries of the world around us. Featuring more than 200 authoritative articles written by experts in their respective fields, the encyclopedia includes a Reader's Guide that organizes entries by broad themes; lists of Further Readings and cross-references that conclude each article; and a Resource Guide listing classic books in the field, leading journals, associations, and key websites.
Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation
CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.