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210123 ||| eng |
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|a 1118940032
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|a 1118940016
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|a 9781118940037
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|a 1118940024
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|a 9781118940020
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|a QA76.9.D343
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|a Anderson, Alan
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|a Statistics for big data for dummies
|c by Alan Anderson with David Semmelroth
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| 260 |
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|a Hoboken, NJ
|b John Wiley and Sons, Inc.
|c 2015
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| 300 |
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|a 1 online resource
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|a VelocityVariety; Traditional Database Management Systems (DBMS); Relational model databases; Hierarchical model databases; Network model databases; Alternatives to traditional database systems; Chapter 3 Using Big Data: The Hot Applications; Big Data and Weather Forecasting; Big Data and Healthcare Services; Big Data and Insurance; Big Data and Finance; Big Data and Electric Utilities; Big Data and Higher Education; Big Data and Retailers; Nordstrom; Walmart; Amazon.com; Big Data and Search Engines; Big Data and Social Media; Chapter 4 Understanding Probabilities
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|a Title Page; Copyright Page; Table of Contents; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go From Here; Part I Introducing Big Data Statistics; Chapter 1 What Is Big Data and What Do You Do with It?; Characteristics of Big Data; Exploratory Data Analysis (EDA); Graphical EDA techniques; Quantitative EDA techniques; Statistical Analysis of Big Data; Probability distributions; Regression analysis; Time series analysis; Forecasting techniques; Chapter 2 Characteristics of Big Data: The Three Vs; Characteristics of Big Data; Volume
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|a Checking discrete dataChecking continuous data; Frequently Encountered Data Headaches; Missing values; Duplicate records; Other Common Data Transformations; Percentiles; Standard scores; Dummy variables; Chapter 7 Figuring the Format: Important Computer File Formats; Spreadsheet Formats; Comma-separated variables (.csv); Text; Microsoft Excel; Web formats; Database Formats; Microsoft Access (.accdb); MySQL (.frm); Chapter 8 Checking Assumptions: Testing for Normality; Goodness of fit test; The chi-square distribution; The null and alternative hypotheses; The level of significance
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|a The alternative hypothesisThe level of significance; The test statistic; The critical value (s); To reject or not to reject, that is the question; Measures of association; Higher-Order Measures; Skewness; Kurtosis; Part II Preparing and Cleaning Data; Chapter 6 Dirty Work: Preparing Your Data for Analysis; Passing the Eye Test: Does Your Data Look Correct?; Checking your sources; Verifying formats; Typecasting your data; Being Careful with Dates; Dealing with datetime formats; Taking geography into account; How your software thinks about dates; Does the Data Make Sense?
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|a Includes bibliographical references and index
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|a The Core Structure: Probability SpacesDiscrete Probability Distributions; Counting outcomes; When only two things can happen: The binomial distribution; Continuous Probability Distributions; The normal distribution; Introducing Multivariate Probability Distributions; Joint probabilities; Unconditional probabilities; Conditional probabilities; Chapter 5 Basic Statistical Ideas; Some Preliminaries Regarding Data; Nominal data; Ordinal data; Summary Statistical Measures; Measures of central tendency; Measures of dispersion; Overview of Hypothesis Testing; The null hypothesis
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| 653 |
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|a Data Mining / bisacsh/2022
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| 653 |
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|a Données volumineuses / Méthodes statistiques
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| 653 |
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|a COMPUTERS. / bisacsh/2022
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| 653 |
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|a Databases / bisacsh/2022
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| 653 |
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|a COMPUTERS / General / bisacsh
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| 653 |
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|a Big data / Statistical methods
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| 700 |
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|a Semmelroth, David
|e author
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a For dummies
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| 015 |
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|a GBB506833
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| 015 |
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|a GBB504761
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| 776 |
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|z 1118940024
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|z 9781118940037
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|z 9781118940020
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| 776 |
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|z 9781118940013
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| 776 |
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|z 9781118940013
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| 776 |
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|z 1118940016
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| 776 |
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|z 1118940032
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781118940013/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3/12
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| 520 |
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|a Big data figures into everything from weather forecasting to political polling. You'll get a handle on the statistical methods used when working with big data, applications for it, ways to organize and check data, and a whole lot more. You will find out what big data is, characteristics that define it, how it's used, and what it makes possible; how to handle it by exploring statistical techniques used with big data, including probability distributions, regression analysis, time series analysis, and forecasting techniques; learn how big data can be analyzed with graphical techniques and how to identify valid, useful, and understandable patterns in data; examine key univariate and multivariate statistical techniques for analyzing data; discover techniques for forecasting the future values of a dataset; learn about the best software packages and programming tools for analyzing statistical data. --
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