Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science FT Press Analytics 1 Thomas W Miller eBook Kostenlose Bücher INE

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Kostenlose Bücher online zu lesen Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science FT Press Analytics 1 Thomas W Miller eBook GUW

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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  
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Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science FT Press Analytics 1 Thomas W Miller eBook Reviews :

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  

ebook,Thomas W. Miller,Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science (FT Press Analytics),Pearson FT Press,Programming Languages - Python,BUSINESS ECONOMICS / Production Operations Management,BUSINESS ECONOMICS / Statistics,BUSINESS STATISTICS,Business Economics,Business Economics/Decision Making Problem Solving,Business Economics/Production Operations Management,Business Economics/Statistics,Business mathematics systems,COMPUTER,COMPUTERS / Databases / Data Mining,COMPUTERS / Programming Languages / Python,Computer Books Database,Computer/Databases,Computers,Computers - Data Base Management,DATABASES,Databases - Data Mining,Monograph Series, any,Non-Fiction,Operations Research,PYTHON (PROGRAMMING LANGUAGE),Programming Languages - Python,Scholarly/Undergraduate,Statistics,TECHNOLOGY ENGINEERING / Operations Research,Technology Engineering/Operations Research,predictive analytics; business analytics; optimization; data mining; data analysis; data exploration; data modeling; R code; applied statistics; statistical research; operations research; quantitative methods; management science; data science; decision science; decision modeling,BUSINESS ECONOMICS / Production Operations Management,BUSINESS ECONOMICS / Statistics,Business Economics/Decision Making Problem Solving,Business Economics/Production Operations Management,Business Economics/Statistics,COMPUTERS / Databases / Data Mining,COMPUTERS / Programming Languages / Python,Databases - Data Mining,Operations Research,Statistics,TECHNOLOGY ENGINEERING / Operations Research,Technology Engineering/Operations Research,Computers - Data Base Management,Business Statistics,Databases,Computers,Computer Books Database,Business mathematics systems

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Product details File Size 85210 KB Print Length 426 pages Simultaneous Device Usage Up to 5 simultaneous devices, per publisher limits Publisher Pearson FT Press; 1 edition (September 29, 2014) Publication Date September 29, 2014 Sold by  Services LLC Language English ASIN B00O121XZ4

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