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Author: G. Elliott Morris
Publisher: W. W. Norton & Company
ISBN: 039386698X
Category : Political Science
Languages : en
Pages : 178
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Book Description
An insightful exploration of political polling and a bold defense of its crucial role in a modern democracy. Public opinion polling is the ultimate democratic process; it gives every person an equal voice in letting elected leaders know what they need and want. But in the eyes of the public, polls today are tarnished. Recent election forecasts have routinely missed the mark and media coverage of polls has focused solely on their ability to predict winners and losers. Polls deserve better. In Strength in Numbers, data journalist G. Elliott Morris argues that the larger purpose of political polls is to improve democracy, not just predict elections. Whether used by interest groups, the press, or politicians, polling serves as a pipeline from the governed to the government, giving citizens influence they would otherwise lack. No one who believes in democracy can afford to give up on polls; they should commit, instead, to understanding them better. In a vibrant history of polling, Morris takes readers from the first semblance of data-gathering in the ancient world through to the development of modern-day scientific polling. He explains how the internet and “big data” have solved many challenges in polling—and created others. He covers the rise of polling aggregation and methods of election forecasting, reveals how data can be distorted and misrepresented, and demystifies the real uncertainty of polling. Candidly acknowledging where polls have gone wrong in the past, Morris charts a path for the industry’s future where it can truly work for the people. Persuasively argued and deeply researched, Strength in Numbers is an essential guide to understanding and embracing one of the most important and overlooked democratic institutions in the United States.
Author: Paul D. McNicholas
Publisher: CRC Press
ISBN: 1351013661
Category : Business & Economics
Languages : en
Pages : 220
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Book Description
"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Features: Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data. Discusses several important topics in data science including supervised and unsupervised learning. Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results. Presents how to optimize Julia code for performance. Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required). The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science. "This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist." Professor Charles Bouveyron INRIA Chair in Data Science Université Côte d’Azur, Nice, France
Author: G. Elliott Morris
Publisher: W. W. Norton & Company
ISBN: 039386698X
Category : Political Science
Languages : en
Pages : 178
Get Book Here
Book Description
An insightful exploration of political polling and a bold defense of its crucial role in a modern democracy. Public opinion polling is the ultimate democratic process; it gives every person an equal voice in letting elected leaders know what they need and want. But in the eyes of the public, polls today are tarnished. Recent election forecasts have routinely missed the mark and media coverage of polls has focused solely on their ability to predict winners and losers. Polls deserve better. In Strength in Numbers, data journalist G. Elliott Morris argues that the larger purpose of political polls is to improve democracy, not just predict elections. Whether used by interest groups, the press, or politicians, polling serves as a pipeline from the governed to the government, giving citizens influence they would otherwise lack. No one who believes in democracy can afford to give up on polls; they should commit, instead, to understanding them better. In a vibrant history of polling, Morris takes readers from the first semblance of data-gathering in the ancient world through to the development of modern-day scientific polling. He explains how the internet and “big data” have solved many challenges in polling—and created others. He covers the rise of polling aggregation and methods of election forecasting, reveals how data can be distorted and misrepresented, and demystifies the real uncertainty of polling. Candidly acknowledging where polls have gone wrong in the past, Morris charts a path for the industry’s future where it can truly work for the people. Persuasively argued and deeply researched, Strength in Numbers is an essential guide to understanding and embracing one of the most important and overlooked democratic institutions in the United States.
Author: Yoni Nazarathy
Publisher: Springer Nature
ISBN: 3030709019
Category : Computers
Languages : en
Pages : 527
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Book Description
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."
ISBN: 1491904399
Category : Computers
Languages : en
Pages : 336
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Book Description
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Author: Allen B. Downey
Publisher: "O'Reilly Media, Inc."
ISBN: 1491907363
Category : Computers
Languages : en
Pages : 284
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Book Description
If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data
Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1119626110
Category : Computers
Languages : en
Pages : 768
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Book Description
Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!
Author: Stephen Klosterman
Publisher: Packt Publishing Ltd
ISBN: 1800569440
Category : Computers
Languages : en
Pages : 433
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Book Description
Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost Key FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook Description If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you'll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you'll experience in real-world data science projects. You'll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you'll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data. What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is for Data Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you're keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
Author: Writers of SciLance
Publisher: Da Capo Lifelong Books
ISBN: 0738216577
Category : Science
Languages : en
Pages : 324
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Book Description
Popular science writing has exploded in the past decade, both in print and online. Who better to guide writers striving to succeed in the profession than a group of award-winning independent journalists with a combined total of 225 years of experience? From Thomas Hayden's chapter on the perfect pitch to Emma Maris's advice on book proposals to Mark Schrope's essential information on contracts, the members of SciLance give writers of all experience levels the practical information they need to succeed, as either a staffer or a freelancer. Going beyond craft, The Science Writer's Handbook also tackles issues such as creating productive office space, balancing work and family, and finding lasting career satisfaction. It is the ultimate guide for anyone looking to prosper as a science writer in the new era of publishing.
Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 0387217363
Category : Mathematics
Languages : en
Pages : 446
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Book Description
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Author: Michael Baron
Publisher: CRC Press
ISBN: 1351697404
Category : Computers
Languages : en
Pages : 486
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Book Description
Praise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and ... could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science, software engineering, telecommunications, and related areas In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping, change-point detection, Bayesian inference, and applications of statistics in epidemiology, clinical trials, semiconductor manufacturing, and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics from the University of Maryland. In his turn, he supervised twelve doctoral students, mostly employed on academic and research positions.
