Analytical Thinking For Ai And Data Science

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Analytical Skills for AI and Data Science

Author : Daniel Vaughan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492060895
Total Pages : 300 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Analytical Skills for AI and Data Science by : Daniel Vaughan

Download or read book Analytical Skills for AI and Data Science written by Daniel Vaughan and published by "O'Reilly Media, Inc.". This book was released on 2020-05-21 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. Break business decisions into stages that can be tackled using different skills from the analytical toolbox Identify and embrace uncertainty in decision making and protect against common human biases Customize optimal decisions to different customers using predictive and prescriptive methods and technologies Ask business questions that create high value through AI- and data-driven technologies

Analytical Skills for AI and Data Science

Author : Daniel Vaughan
Publisher : O'Reilly Media
ISBN 13 : 9781492060949
Total Pages : 250 pages
Book Rating : 4.0/5 (69 download)

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Book Synopsis Analytical Skills for AI and Data Science by : Daniel Vaughan

Download or read book Analytical Skills for AI and Data Science written by Daniel Vaughan and published by O'Reilly Media. This book was released on 2020-08-11 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: While several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the potential of this predictive revolution? This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems. Author Daniel Vaughan shows practitioners of data science and others interested in using AI not only how to ask the right questions but also how to generate value from data and analytics using modern AI technologies and decision theory principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. With this book, you'll learn how to: Break business decisions into stages and use predictive or prescriptive methods on each stage Identify human biases when working with uncertainty Customize optimal decisions to different customers using predictive and prescriptive methods Ask business questions with high potential for value creation through AI and data-driven methods Simplify complexity to tackle difficult business decisions with current predictive and prescriptive technologies

Data Science for Business

Author : Foster Provost
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 144937428X
Total Pages : 506 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Data Science for Business by : Foster Provost

Download or read book Data Science for Business written by Foster Provost and published by "O'Reilly Media, Inc.". This book was released on 2013-07-27 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value

Author : Eric Anderson
Publisher : McGraw Hill Professional
ISBN 13 : 1260459152
Total Pages : 353 pages
Book Rating : 4.2/5 (64 download)

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Book Synopsis Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value by : Eric Anderson

Download or read book Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value written by Eric Anderson and published by McGraw Hill Professional. This book was released on 2020-11-23 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.

Analytical Thinking for AI and Data Science

Author : Daniel Vaughan
Publisher :
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4.:/5 (114 download)

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Book Synopsis Analytical Thinking for AI and Data Science by : Daniel Vaughan

Download or read book Analytical Thinking for AI and Data Science written by Daniel Vaughan and published by . This book was released on 2020 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: While several market-leading companies have successfully transformed through data- and AI-driven approaches to business, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the potential of this predictive revolution? This practical guide presents a battle-tested method to help you translate business decisions into tractable descriptive, predictive, and prescriptive problems. Author Daniel Vaughan shows practitioners of data science and others interested in using AI not only how to ask the right questions but also how to generate value from data and analytics using modern AI technologies and decision theory principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. With this book, you'll learn how to: Break business decisions into stages and use predictive or prescriptive methods on each stage Identify human biases when working with uncertainty Customize optimal decisions to different customers using predictive and prescriptive methods Ask business questions with high potential for value creation through AI and data-driven methods Simplify complexity to tackle difficult business decisions with current predictive and prescriptive technologies.

Data Science Thinking

Author : Longbing Cao
Publisher : Springer
ISBN 13 : 3319950924
Total Pages : 404 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Data Science Thinking by : Longbing Cao

Download or read book Data Science Thinking written by Longbing Cao and published by Springer. This book was released on 2018-08-17 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

Data Science Applied to Sustainability Analysis

Author : Jennifer Dunn
Publisher : Elsevier
ISBN 13 : 0128179775
Total Pages : 312 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Data Science Applied to Sustainability Analysis by : Jennifer Dunn

Download or read book Data Science Applied to Sustainability Analysis written by Jennifer Dunn and published by Elsevier. This book was released on 2021-05-11 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery Includes considerations sustainability analysts must evaluate when applying big data Features case studies illustrating the application of data science in sustainability analyses

Advanced Analytics and AI

Author : Tony Boobier
Publisher : John Wiley & Sons
ISBN 13 : 1119390931
Total Pages : 314 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Advanced Analytics and AI by : Tony Boobier

Download or read book Advanced Analytics and AI written by Tony Boobier and published by John Wiley & Sons. This book was released on 2018-04-03 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Be prepared for the arrival of automated decision making Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses. Cognitive Analysis and AI prepares business users for the era of cognitive analytics / artificial intelligence. Building on established texts and commentary, it specifically prepares you in terms of expectation, impact on personal roles, and responsibilities. It focuses on the specific impact on key industries (retail, financial services, utilities and media) and also on key professions (such as accounting, operational management, supply chain and risk management). Shows you how users interact with the system in natural language Explains how cognitive analysis/AI can source ‘big data’ Provides a roadmap for implementation Gets you up to speed now before you get left behind If you’re a decision maker or budget holder within the corporate context, this invaluable book helps you gain an advantage from the deployment of cognitive analytics tools.

Doing Data Science

Author : Cathy O'Neil
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 144936389X
Total Pages : 408 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Doing Data Science by : Cathy O'Neil

Download or read book Doing Data Science written by Cathy O'Neil and published by "O'Reilly Media, Inc.". This book was released on 2013-10-09 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Ethics and Data Science

Author : Mike Loukides
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492078212
Total Pages : 37 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Ethics and Data Science by : Mike Loukides

Download or read book Ethics and Data Science written by Mike Loukides and published by "O'Reilly Media, Inc.". This book was released on 2018-07-25 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Data Science for Business

Author : Foster Provost
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449374298
Total Pages : 414 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Data Science for Business by : Foster Provost

Download or read book Data Science for Business written by Foster Provost and published by "O'Reilly Media, Inc.". This book was released on 2013-07-27 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation This broad, deep, but not-too-technical guide introduces you to 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. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.

Data Science: The Hard Parts

Author : Daniel Vaughan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098146441
Total Pages : 257 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Data Science: The Hard Parts by : Daniel Vaughan

Download or read book Data Science: The Hard Parts written by Daniel Vaughan and published by "O'Reilly Media, Inc.". This book was released on 2023-11 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Artificial Intelligence and Legal Analytics

Author : Kevin D. Ashley
Publisher : Cambridge University Press
ISBN 13 : 1107171504
Total Pages : 451 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Artificial Intelligence and Legal Analytics by : Kevin D. Ashley

Download or read book Artificial Intelligence and Legal Analytics written by Kevin D. Ashley and published by Cambridge University Press. This book was released on 2017-07-10 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems.

Data Science: The Hard Parts

Author : Daniel Vaughan
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098146433
Total Pages : 244 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Data Science: The Hard Parts by : Daniel Vaughan

Download or read book Data Science: The Hard Parts written by Daniel Vaughan and published by "O'Reilly Media, Inc.". This book was released on 2023-11-01 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).

Encyclopedia of Data Science and Machine Learning

Author : Wang, John
Publisher : IGI Global
ISBN 13 : 1799892212
Total Pages : 3296 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Encyclopedia of Data Science and Machine Learning by : Wang, John

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

AI-Enabled Analytics for Business

Author : Lawrence S. Maisel
Publisher : John Wiley & Sons
ISBN 13 : 1119736080
Total Pages : 243 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis AI-Enabled Analytics for Business by : Lawrence S. Maisel

Download or read book AI-Enabled Analytics for Business written by Lawrence S. Maisel and published by John Wiley & Sons. This book was released on 2022-01-19 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities Empower better, smarter decision making by implementing AI-enabled analytics decision support tools Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation.