Data Science for Business

Data Science for Business Author Foster Provost
ISBN-10 9781449374280
Release 2013-07-27
Pages 414
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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



Web and Network Data Science

Web and Network Data Science Author Thomas W. Miller
ISBN-10 9780133887648
Release 2014-12-19
Pages 384
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Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.



Modeling Techniques in Predictive Analytics with Python and R

Modeling Techniques in Predictive Analytics with Python and R Author Thomas W. Miller
ISBN-10 9780133892147
Release 2014-09-29
Pages 448
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more



Modeling Techniques in Predictive Analytics

Modeling Techniques in Predictive Analytics Author Thomas W. Miller
ISBN-10 9780133886191
Release 2014-09-29
Pages 384
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To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more



big data work

big data   work Author Thomas H. Davenport
ISBN-10 9783800648153
Release 2014-10-15
Pages 214
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Big Data in Unternehmen. Dieses neue Buch gibt Managern ein umfassendes Verständnis dafür, welche Bedeutung Big Data für Unternehmen zukünftig haben wird und wie Big Data tatsächlich genutzt werden kann. Am Ende jedes Kapitels aktivieren Fragen, selbst nach Lösungen für eine erfolgreiche Implementierung und Nutzung von Big Data im eigenen Unternehmen zu suchen. Die Schwerpunkte - Warum Big Data für Sie und Ihr Unternehmen wichtig ist - Wie Big Data Ihre Arbeit, Ihr Unternehmen und Ihre Branche verändern - - wird - Entwicklung einer Big Data-Strategie - Der menschliche Aspekt von Big Data - Technologien für Big Data - Wie Sie erfolgreich mit Big Data arbeiten - Was Sie von Start-ups und Online-Unternehmen lernen können - Was Sie von großen Unternehmen lernen können: Big Data und Analytics 3.0 Der Experte Thomas H. Davenport ist Professor für Informationstechnologie und -management am Babson College und Forschungswissenschaftler am MIT Center for Digital Business. Zudem ist er Mitbegründer und Forschungsdirektor am International Institute for Analytics und Senior Berater von Deloitte Analytics.



Einf hrung in Data Science

Einf  hrung in Data Science Author Joel Grus
ISBN-10 9783960100256
Release 2016-03-31
Pages 352
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Dieses Buch führt Sie in Data Science ein, indem es grundlegende Prinzipien der Datenanalyse erläutert und Ihnen geeignete Techniken und Werkzeuge vorstellt. Sie lernen nicht nur, wie Sie Bibliotheken, Frameworks, Module und Toolkits konkret einsetzen, sondern implementieren sie auch selbst. Dadurch entwickeln Sie ein tieferes Verständnis für die Zusammenhänge und erfahren, wie essenzielle Tools und Algorithmen der Datenanalyse im Kern funktionieren. Falls Sie Programmierkenntnisse und eine gewisse Sympathie für Mathematik mitbringen, unterstützt Joel Grus Sie dabei, mit den mathematischen und statistischen Grundlagen der Data Science vertraut zu werden und sich Programmierfähigkeiten anzueignen, die Sie für die Praxis benötigen. Dabei verwendet er Python: Die weitverbreitete Sprache ist leicht zu erlernen und bringt zahlreiche Bibliotheken für Data Science mit. Aus dem Inhalt: - Absolvieren Sie einen Crashkurs in Python - Lernen Sie die Grundlagen von linearer Algebra, Statistik und Wahrscheinlichkeitsrechnung kennen und erfahren Sie, wie diese in Data Science eingesetzt werden - Sammeln, untersuchen, bereinigen, bearbeiten und manipulieren Sie Daten - Tauchen Sie in die Welt des maschinellen Lernens ein - Implementieren Sie Modelle wie k-nearest Neighbors, Naive Bayes, lineare und logistische Regression, Entscheidungsbäume, neuronale Netzwerke und Clustering - Entdecken Sie Empfehlungssysteme, Sprachverarbeitung, Netzwerkanalyse, MapReduce und Datenbanken



Data Science

Data Science Author Michael Oettinger
ISBN-10 9783743936294
Release 2017-07-10
Pages 208
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Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen "Analytics"-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.



Machine Learning mit Python

Machine Learning mit Python Author Sebastian Raschka
ISBN-10 9783958454248
Release 2016-11-22
Pages 424
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Machine Learning mit Python has been writing in one form or another for most of life. You can find so many inspiration from Machine Learning mit Python also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Machine Learning mit Python book for free.



Machine Learning

Machine Learning Author Jason Bell
ISBN-10 9781118889060
Release 2014-11-03
Pages 408
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Machine Learning has been writing in one form or another for most of life. You can find so many inspiration from Machine Learning also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Machine Learning book for free.



Big Data MBA

Big Data MBA Author Bill Schmarzo
ISBN-10 9781119181385
Release 2015-12-11
Pages 312
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Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.



Statistik f r Dummies

Statistik f  r Dummies Author Deborah J. Rumsey
ISBN-10 9783527692767
Release 2016-12-16
Pages 355
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Statistik ist ganz sicher kein beliebtes, aber ein notwendiges und auch n?tzliches Thema. Deborah Rumsey erkl?rt Ihnen in diesem Buch die notwendigen Grundbegriffe, erl?utert die wichtigsten statistischen Konzepte und schafft einen Bezug zwischen Theorie und Praxis. Dabei kommt Sie fast ohne Formeln aus. Sie lernen die verschiedenen grafischen Darstellungsm?glichkeiten von statistischem Material kennen und erfahren, wie Sie Ihre Ergebnisse richtig auswerten. Egal ob Mittelwert, Bias, Standardabweichung oder Konfidenzintervall, schon bald kann Ihnen keiner mehr etwas vormachen.



K nstliche Intelligenz

K  nstliche Intelligenz Author Stuart J. Russell
ISBN-10 3827370892
Release 2004
Pages 1327
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K nstliche Intelligenz has been writing in one form or another for most of life. You can find so many inspiration from K nstliche Intelligenz also informative, and entertaining. Click DOWNLOAD or Read Online button to get full K nstliche Intelligenz book for free.



Das Facebook Marketing Buch

Das Facebook Marketing Buch Author Dan Zarrella
ISBN-10 9783897215955
Release 2011
Pages 296
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Wer heute eine Marketing-Strategie entwickelt, zieht in der Regel auch ein Engagement auf Facebook in Betracht. Zurecht, denn für die Meinungsbildung und den Austausch über Unternehmen, Marken und Produkte spielt diese Plattform eine immer größere Rolle. Allein die Reichweite beeindruckt: Weltweit nutzen zurzeit 500 Millionen Menschen Facebook, in Deutschland sind es fast 14 Millionen. Doch welche Möglichkeiten bietet Facebook dem Marketing? Wo liegen die Chancen und wo die Gefahren? Wie entwickelt man eine Kampagne, und wie lässt sich ihr Erfolg messen? Darüber klärt Das Facebook Marketing-Buch auf. Anschaulich, praxisnah und gut verständlich werden zunächst die Facebook-Basics erklärt, dann ihre mögliche Rolle im Marketing-Mix. Weitere zentrale Themen sind Strategieentwicklung, Monitoring und Erfolgskontrolle.



SQL f r Dummies

SQL f  r Dummies Author Allen G. Taylor
ISBN-10 9783527810895
Release 2017-04-24
Pages 440
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SQL f r Dummies has been writing in one form or another for most of life. You can find so many inspiration from SQL f r Dummies also informative, and entertaining. Click DOWNLOAD or Read Online button to get full SQL f r Dummies book for free.



Einf hrung in Python

Einf  hrung in Python Author Mark Lutz
ISBN-10 9783897214880
Release 2007
Pages 624
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Einf hrung in Python has been writing in one form or another for most of life. You can find so many inspiration from Einf hrung in Python also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Einf hrung in Python book for free.



Einstein s Dreams

Einstein s Dreams Author Alan Lightman
ISBN-10 9783426424810
Release 2013-12-02
Pages 208
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Albert Einstein träumt. Von der Zeit, wovon sonst? Dreißig Mal nickt er ein, und jedes Mal erscheint vor seinem geistigen Auge eine neue Vision jenes sonderbaren Phänomens, in das unsere Existenz gebettet ist. Alan Lightman ist mit seinem Roman ein Faszinosum gelungen: Mit spielerischer Leichtigkeit begleiten wir das Jahrhundertgenie Einstein auf wundersam erhellenden Traumreisen durch die Zeit. »Dreißig kleine, federleichte Gedankenspiele ... ein wunderschöner, anrührender und saukomischer Erstling.« (Stern)



Predictive Analytics f r Dummies

Predictive Analytics f  r Dummies Author Anasse Bari
ISBN-10
Release 2016-05-11
Pages 360
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Predictive Analytics f r Dummies has been writing in one form or another for most of life. You can find so many inspiration from Predictive Analytics f r Dummies also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Predictive Analytics f r Dummies book for free.