Statistics for Big Data For Dummies

Statistics for Big Data For Dummies

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  • Author: Alan Anderson
  • Publisher: John Wiley & Sons
  • ISBN: 1118940016
  • Category : Computers
  • Languages : en
  • Pages : 390

The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.


Big Data For Dummies

Big Data For Dummies

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  • Author: Judith S. Hurwitz
  • Publisher: John Wiley & Sons
  • ISBN: 1118644174
  • Category : Computers
  • Languages : en
  • Pages : 336

Find the right big data solution for your business ororganization Big data management is one of the major challenges facingbusiness, industry, and not-for-profit organizations. Data setssuch as customer transactions for a mega-retailer, weather patternsmonitored by meteorologists, or social network activity can quicklyoutpace the capacity of traditional data management tools. If youneed to develop or manage big data solutions, you'll appreciate howthese four experts define, explain, and guide you through this newand often confusing concept. You'll learn what it is, why itmatters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importanceto businesses, not-for-profit organizations, government, and ITprofessionals Authors are experts in information management, big data, and avariety of solutions Explains big data in detail and discusses how to select andimplement a solution, security concerns to consider, data storageand presentation issues, analytics, and much more Provides essential information in a no-nonsense,easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helpsyou take charge of big data solutions for your organization.


Getting a Big Data Job For Dummies

Getting a Big Data Job For Dummies

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  • Author: Jason Williamson
  • Publisher: John Wiley & Sons
  • ISBN: 1118903846
  • Category : Computers
  • Languages : en
  • Pages : 264

Hone your analytic talents and become part of the next big thing Getting a Big Data Job For Dummies is the ultimate guide to landing a position in one of the fastest-growing fields in the modern economy. Learn exactly what "big data" means, why it's so important across all industries, and how you can obtain one of the most sought-after skill sets of the decade. This book walks you through the process of identifying your ideal big data job, shaping the perfect resume, and nailing the interview, all in one easy-to-read guide. Companies from all industries, including finance, technology, medicine, and defense, are harnessing massive amounts of data to reap a competitive advantage. The demand for big data professionals is growing every year, and experts forecast an estimated 1.9 million additional U.S. jobs in big data by 2015. Whether your niche is developing the technology, handling the data, or analyzing the results, turning your attention to a career in big data can lead to a more secure, more lucrative career path. Getting a Big Data Job For Dummies provides an overview of the big data career arc, and then shows you how to get your foot in the door with topics like: The education you need to succeed The range of big data career path options An overview of major big data employers A plan to develop your job-landing strategy Your analytic inclinations may be your ticket to long-lasting success. In a highly competitive job market, developing your data skills can create a situation where you pick your employer rather than the other way around. If you're ready to get in on the ground floor of the next big thing, Getting a Big Data Job For Dummies will teach you everything you need to know to get started today.


Big Data Analytics

Big Data Analytics

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  • Author: Saumyadipta Pyne
  • Publisher: Springer
  • ISBN: 8132236289
  • Category : Computers
  • Languages : en
  • Pages : 276

This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.


Big Data

Big Data

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  • Author: Cornelia Hammer
  • Publisher: International Monetary Fund
  • ISBN: 148431090X
  • Category : Business & Economics
  • Languages : en
  • Pages : 41

Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.


Handbook of Big Data

Handbook of Big Data

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  • Author: Peter Bühlmann
  • Publisher: CRC Press
  • ISBN: 9780367330736
  • Category : Big data
  • Languages : en
  • Pages : 0

This handbook provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from statistics and computer science experts in industry and academia, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice.


Predictive Analytics For Dummies

Predictive Analytics For Dummies

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  • Author: Anasse Bari
  • Publisher: John Wiley & Sons
  • ISBN: 1118729412
  • Category : Business & Economics
  • Languages : en
  • Pages : 371

Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.


Classification, (Big) Data Analysis and Statistical Learning

Classification, (Big) Data Analysis and Statistical Learning

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  • Author: Francesco Mola
  • Publisher: Springer
  • ISBN: 3319557084
  • Category : Mathematics
  • Languages : en
  • Pages : 242

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.


Handbook of Big Data Analytics

Handbook of Big Data Analytics

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  • Author: Wolfgang Karl Härdle
  • Publisher: Springer
  • ISBN: 3319182846
  • Category : Computers
  • Languages : en
  • Pages : 538

Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.


Artificial Intelligence, Big Data and Data Science in Statistics

Artificial Intelligence, Big Data and Data Science in Statistics

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  • Author: Ansgar Steland
  • Publisher: Springer Nature
  • ISBN: 3031071557
  • Category : Mathematics
  • Languages : en
  • Pages : 378

This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.