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Numsense! Data Science for the Layman

Author: Annalyn Ng

Publisher: Annalyn Ng & Kenneth Soo

Published: 2017-03-24

Total Pages:

ISBN-13: 9789811110689

Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

Data Science in Layman's Terms

Author: Nicholas Lincoln

Publisher:

Published: 2018-07-02

Total Pages: 482

ISBN-13: 9780692150757

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Data Science in Layman's Terms: Statistics strikes a balance between an easy-reading tutorial, and a theory intensive textbook. It offers a complete roadmap for understanding statistics. The concepts in this book are explained in plain English, and every chapter contains R and Python code that shows how to implement the concepts.

Doing Data Science

Author: Cathy O'Neil

Publisher: "O'Reilly Media, Inc."

Published: 2013-10-09

Total Pages: 408

ISBN-13: 144936389X

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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.

Algorithms: The Building Blocks of Computer Programming

Author: Daniel R. Faust

Publisher: The Rosen Publishing Group, Inc

Published: 2018-07-15

Total Pages: 34

ISBN-13: 1538331276

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Algorithms might sound like a complicated tech term, but don't be intimidated, people actually use them every day. Using accessible language and full-color photographs, this book simplifies algorithms in an easy-to-understand way. Readers will be amazed to learn that an algorithm is just a set of steps for computers to follow to get things done. STEM topics from the Next Generation Science Standards are emphasized throughout the text. Sidebars featuring key terms help readers grow their tech vocabulary, and fact boxes provide additional opportunities to learn.

Science and Reason

Author: Henry E. Kyburg Jr.

Publisher: Oxford University Press

Published: 1990-11-15

Total Pages: 296

ISBN-13: 0195362748

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In this work Henry Kyburg presents his views on a wide range of philosophical problems associated with the study and practice of science and mathematics. The main structure of the book consists of a presentation of Kyburg's notions of epistemic probability and its use in the scientific enterprise i.e., the effort to modify previously adopted beliefs in the light of experience. Intended for cognitive scientists and people in artificial intelligence as well as for technically oriented philosophers, the book also provides a general overview of the philosophy of science for the non-philosopher by one of the leading authorities in the field.

Evolutionary Psychology 101

Author: Glenn Geher, PhD

Publisher: Springer Publishing Company

Published: 2013-10-10

Total Pages: 234

ISBN-13: 0826107192

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ìAt long last, a readable, accessible, user friendly introduction to evolutionary psychology written by a rising star in the field. This book, filled with a broad array of fascinating topics, is bound to further whet the appetite of a growing number of students who have been inspired by this provocative, yet eminently testable approach to human behavior.î Gordon G. Gallup Jr., PhD University at Albany "A frolicking, down-to-earth, and informative introduction to the ever evolving and controversial field of evolutionary psychology." Scott Barry Kaufman, PhD Author, Ungifted: Intelligence Redefined ìGlenn Geher has created a text that is both comprehensive in coverage and scope and very accessible. It should be a welcome addition to the field that serves to further individuals' understanding of Evolutionary Psychology.î T. Joel Wade, PhD Professor and Chair of Psychology, Bucknell University This is a concise and student-friendly survey of the burgeoning field of evolutionary psychology (EP) and the controversies that surround it. Evolutionary psychology is an approach to studying human behavior that is rooted in modern evolutionary theory. Firmly grounded in the theoretical and research literature of EP, the book addresses the core theories, approaches, applications, and current findings that comprise this discipline. It is unique in its interdisciplinary focus, which encompasses EPís impact on both psychological and non-psychological disciplines. Written by an eminent evolutionary psychologist who is President of the Northeastern Evolutionary Psychology Society, the text examines psychological processes that lead to human survival and those that may lead to reproductive benefitsósometimes even at a cost to survival. It cites a rich body of literature that provides insights into the role of sexual selection in shaping the human mind. The text presents current research on such important domains of EP as childhood, courtship, intrasexual competition, sex, pair-bonding, parenting, familial relations, non-familial relations, aggression, and altruism. Considering the potential of EP to mitigate some of our greatest social problems, the text examines the ways in which EP can be applied to society and religion. It also offers a thoughtful, balanced approach to such controversies in EP as the issues of genetic determinism, racism, and sexism. Key Features: Provides a broad survey one of the most recent, widely researched, and controversial fields to emerge in psychology over the past 20 years Written by an eminent evolutionary psychologist who is President of the Northeastern Evolutionary Psychology Society Presents EP concepts in an accessible, student-friendly way Offers a unique interdisciplinary focus that addresses the impact of EP on both psychological and non-psychological disciplines Emphasizes controversies within the field of evolutionary psychology and includes critiques of EP from people outside this discipline

Transitions and Trees

Author: Hans Hüttel

Publisher: Cambridge University Press

Published: 2010-04-29

Total Pages: 291

ISBN-13: 1139788590

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Structural operational semantics is a simple, yet powerful mathematical theory for describing the behaviour of programs in an implementation-independent manner. This book provides a self-contained introduction to structural operational semantics, featuring semantic definitions using big-step and small-step semantics of many standard programming language constructs, including control structures, structured declarations and objects, parameter mechanisms and procedural abstraction, concurrency, nondeterminism and the features of functional programming languages. Along the way, the text introduces and applies the relevant proof techniques, including forms of induction and notions of semantic equivalence (including bisimilarity). Thoroughly class-tested, this book has evolved from lecture notes used by the author over a 10-year period at Aalborg University to teach undergraduate and graduate students. The result is a thorough introduction that makes the subject clear to students and computing professionals without sacrificing its rigour. No experience with any specific programming language is required.

Animal Cognition 101

Author: Erin Colbert-White, PhD

Publisher: Springer Publishing Company

Published: 2019-07-09

Total Pages: 288

ISBN-13: 0826162355

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This concise overview provides a scientific yet highly accessible introduction to the science of animal cognition. Written for undergraduate college courses as well as any interested reader, it encompasses the history of animal cognition studies, essential theories and methodology, and the latest findings and controversies in animal consciousness and communication. The book challenges myths and preconceived notions about animal cognition by explaining the scientific theories, the perils of anthropomorphism, and the value of knowing a species’ natural history before making assumptions or drawing conclusions. Each chapter includes an Animal Spotlight and a Human Application section. The Animal Spotlight highlights individual animals—the “rock stars” of animal cognition—that have made significant contributions to the field. Reminding us that we too are animals, the Human Application sections connect topics in animal cognition to human behavior and cognition. Additionally, the book provides ideas for readers to conduct their own investigations into animal cognition. Key Features Provides a scientific yet accessible introduction to animal cognition studies Examines the development of the field, its theories and methods, and the latest findings and controversies Addresses animal consciousness, communication, social cognition, and cognitive flexibility Highlights individual animals that have made significant contributions to the field Connects topics in animal cognition to human behavior and cognition Provides ideas for readers to conduct their own investigations into animal cognition

Scheffer/Schachtschabel Soil Science

Author: Hans-Peter Blume

Publisher: Springer

Published: 2015-11-24

Total Pages: 618

ISBN-13: 3642309429

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The soils are fundamental to our existence, delivering water and nutrients to plants, that feed us. But they are in many ways in danger and their conservation is therefore a most important focus for science, governments and society as a whole. A team of world recognised researchers have prepared this first English edition based on the 16th European edition. • The precursors and the processes of soil development • The physical, biological and chemical properties of soils • Nutrients and Pollutants • The various soil classifications with the main focus on the World Reference Base for Soil Resources (WRB) • The most important soils and soil landscapes of the world • Soil Evaluation Techniques • Basic Principles of Soil Conservation Whoever works with soils needs this book.

Fundamentals of Data Science

Author: Samuel Burns

Publisher:

Published: 2019-09-17

Total Pages: 134

ISBN-13: 9781693798924

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"This book is for students or anyone, with limited or no prior programming, statistics, and data analytics knowledge. This short guide is ideal for absolute beginners, or anyone who wants to acquire a basic working knowledge of data science. It is an excellent guide if you want to learn about the principals of data science from scratch, in just a few hours. The author discussed everything that you need to know about data science. First, you are guided to learn the meaning of data science. The history of data science has been discussed to help you know how people came to realize that data is a rich source of knowledge and intelligence. The theories underlying data science have been discussed. Examples include decision and estimation theories. The author discussed the various machine learning algorithms used in data science and the various steps one has to undergo when performing data science tasks, from data collection to data presentation and visualization. The author helps you to know the various ways through which you can apply data science in your business for increased profits. A simple language has been used to ensure ease of understanding, especially for beginners." --