Free Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics)

Description Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics)

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. For each problem, Miller explains: Why the problem is significant What data is relevant How to explore your data How to model your data ― first conceptually, with words and figures; and then with mathematics and programs Miller walks through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. Extensive example code is presented in Python, a new and extremely popular language for applied statistics, statistical research, and predictive modeling; all code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics) PDF ePub

Modeling Techniques in Predictive Analytics with Python and R ~ iv Modeling Techniques in Predictive Analytics with Python and R 10 Spatial Data Analysis 211 11 Brand and Price 239 12 The Big Little Data Game 273 A Data Science Methods 277 A.1 Databases and Data Preparation 279 A.2 Classical and Bayesian Statistics 281 A.3 Regression and Classification 284 A.4 Machine Learning 289 A.5 Web and Social Network Analysis 291 A.6 Recommender Systems 293

Modeling Techniques in Predictive Analytics ~ growth of R and the ease of growing it further ensures that the R envi-ronment for modeling techniques in predictive analytics will be around for many years to come. Genie out of the lamp, wizard from behind the curtain—rocket science is not what it usedto be. Secrets are being revealed. This book is part of the process.

Modeling Techniques in Predictive Analytics with Python ~ Description. 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.

Modeling Techniques in Predictive Analytics with Python ~ Web and Network Data Science: Modeling Techniques in Predictive Analytics By Thomas W. Miller Programs and Data to Accompany "Web and Network Data Science: Modeling Techniques in Predictive Analytics" Miller (2015) Note that many R programs contain library commands for bringing in R functions included in packages.

Modeling Techniques in Predictive Analytics with Python ~ Compete on analytics: win by understanding your data more deeply than your competitors do! 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 .

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Marketing Data Science: Modeling Techniques in Predictive ~ Description. In Marketing Data Science, a top faculty member of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics.Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world .

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Marketing Data Science: Modeling Techniques in Predictive ~ Miller’s books include Web and Network Data Science, Modeling Techniques in Predictive Analytics, Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.

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Modeling Techniques in Predictive Analytics with Python and R ~ 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.