The Power Of Data
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| Author | : Robin H. Lock |
| Publisher | : John Wiley & Sons |
| Total Pages | : 866 |
| Release | : 2020-10-13 |
| Genre | : Mathematics |
| ISBN | : 1119682169 |
Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.
| Author | : Jim E. Thatcher |
| Publisher | : Pluto Press (UK) |
| Total Pages | : 144 |
| Release | : 2021-12-20 |
| Genre | : |
| ISBN | : 9780745340074 |
An introduction to learning how to protect ourselves and organise against Big Data
| Author | : Sejal Vora |
| Publisher | : SAGE Publications Pvt. Limited |
| Total Pages | : 0 |
| Release | : 2019-04-30 |
| Genre | : Business & Economics |
| ISBN | : 9789353282905 |
The first-of-its-kind book on data story telling set in the Indian context by an Indian author. The Power of Data Storytelling is a book that aims to solve the classic dilemma of—How do I make company data interesting and present it in the form of a great data story for today’s time-crunched professionals. The book focuses on various methods of converting dry facts and figures into interesting characters, events and relaying them in the form of a story to enable company’s decision-making. The book covers all data story related aspects—art of storytelling, building, writing and visualizing. The book reflects practical corporate examples from varied fields and how data storytelling enabled the decision-making process. It does not require knowledge of sophisticated tools and introduces new, simple and application-oriented methods at every stage to take data storytelling forward. The book has wide application across industries and organizations with data sets that are big and small. It has explanatory written and visual examples at every discussion which makes it less theoretical and more practically applicable.
| Author | : Michael Luca |
| Publisher | : MIT Press |
| Total Pages | : 229 |
| Release | : 2021-03-02 |
| Genre | : Business & Economics |
| ISBN | : 0262542277 |
How tech companies like Google, Airbnb, StubHub, and Facebook learn from experiments in our data-driven world—an excellent primer on experimental and behavioral economics Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of different online experiences. Once an esoteric tool for academic research, the randomized controlled trial has gone mainstream. No tech company worth its salt (or its share price) would dare make major changes to its platform without first running experiments to understand how they would influence user behavior. In this book, Michael Luca and Max Bazerman explain the importance of experiments for decision making in a data-driven world. Luca and Bazerman describe the central role experiments play in the tech sector, drawing lessons and best practices from the experiences of such companies as StubHub, Alibaba, and Uber. Successful experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget—or bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts. Moving beyond tech, Luca and Bazerman consider experimenting for the social good—different ways that governments are using experiments to influence or “nudge” behavior ranging from voter apathy to school absenteeism. Experiments, they argue, are part of any leader's toolkit. With this book, readers can become part of “the experimental revolution.”
| Author | : David Beer |
| Publisher | : SAGE |
| Total Pages | : 269 |
| Release | : 2018-10-29 |
| Genre | : Social Science |
| ISBN | : 1526463199 |
A significant new way of understanding contemporary capitalism is to understand the intensification and spread of data analytics. This text is about the powerful promises and visions that have led to the expansion of data analytics and data-led forms of social ordering. It is centrally concerned with examining the types of knowledge associated with data analytics and shows that how these analytics are envisioned is central to the emergence and prominence of data at various scales of social life. This text aims to understand the powerful role of the data analytics industry and how this industry facilitates the spread and intensification of data-led processes. As such, The Data Gaze is concerned with understanding how data-led, data-driven and data-reliant forms of capitalism pervade organisational and everyday life. Using a clear theoretical approach derived from Foucault and critical data studies, the text develops the concept of the data gaze and shows how powerful and persuasive it is. It’s an essential and subversive guide to data analytics and data capitalism.
| Author | : Catherine D'Ignazio |
| Publisher | : MIT Press |
| Total Pages | : 328 |
| Release | : 2020-03-31 |
| Genre | : Social Science |
| ISBN | : 0262358530 |
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
| Author | : Patrick Bangert |
| Publisher | : Elsevier |
| Total Pages | : 276 |
| Release | : 2021-01-14 |
| Genre | : Technology & Engineering |
| ISBN | : 0128226005 |
Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls
| Author | : Ken Puls |
| Publisher | : Tickling Keys, Inc. |
| Total Pages | : 298 |
| Release | : 2015-06-01 |
| Genre | : Computers |
| ISBN | : 1615473459 |
Power Query is one component of the Power BI (Business Intelligence) product from Microsoft, and "M" is the name of the programming language created by it. As more business intelligence pros begin using Power Pivot, they find that they do not have the Excel skills to clean the data in Excel; Power Query solves this problem. This book shows how to use the Power Query tool to get difficult data sets into both Excel and Power Pivot, and is solely devoted to Power Query dashboarding and reporting.
| Author | : Ali Tajer |
| Publisher | : Cambridge University Press |
| Total Pages | : 601 |
| Release | : 2021-04-08 |
| Genre | : Computers |
| ISBN | : 1108494757 |
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
| Author | : Reza Arghandeh |
| Publisher | : Elsevier |
| Total Pages | : 450 |
| Release | : 2024-07-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0443219516 |
Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. - Provides a total refresh to include the most up-to-date research, developments, and challenges - Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data - Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics - Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data
