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Modeling techniques in predictive analytics with Python and R: a guide to data science

By: Miller, Thomas W.
Publisher: New Jersey Pearson Education 2015Description: xviii, 418 p.ISBN: 9780133892062.Subject(s): R (Computer program language) | Python (Computer program language) | Business forecasting - Data processing | Business forecasting - Mathematical modelsDDC classification: 519.5 Summary: Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modeling Techniques in Predictive Analytics, the Python edition, the leader of Northwestern University’s prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics. Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. (http://www.pearsonhighered.com/pearsonhigheredus/educator/product/products_detail.page?isbn=9780133892062&forced_logout=forced_logged_out)
List(s) this item appears in: VR_Healthcare Analytics | Big data | VR_Data Analytics, Data Visualization and Big Data
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Books Vikram Sarabhai Library
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Non-fiction 519.5 M4M6 (Browse shelf) Available 188621

Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modeling Techniques in Predictive Analytics, the Python edition, the leader of Northwestern University’s prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.
Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, Web and text analytics, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data.
(http://www.pearsonhighered.com/pearsonhigheredus/educator/product/products_detail.page?isbn=9780133892062&forced_logout=forced_logged_out)

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