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1. EPUB Download Marketing Data Science:
Modeling Techniques in Predictive Analytics
with R and Python Full Unlimited
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2. Book 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
applications. Building on his predictive analytics program at Northwestern, Miller covers
segmentation, target marketing, brand and product positioning, new product development, choice
modeling, recommender systems, pricing research, retail site selection, demand estimation, sales
forecasting, customer retention, and lifetime value analysis. Starting where his widely-praised
Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights
that were previously segregated in texts on web analytics, network science, information
technology, and programming. Coverage includes: The role of analytics in delivering effective
messages on the web Understanding the web by understanding its hidden structures Being
recognized on the web -- and watching your own competitors Visualizing networks and
understanding communities within them Measuring sentiment and making recommendations
Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics,
regression/classification, machine learning, and text analytics Six complete case studies address
exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-
relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data,
and more. This text's extensive set of web and network problems draw on rich public-domain data
sources; many are accompanied by solutions in Python and/or R.
EPUB Download Marketing Data Science: Modeling Techniques in Predictive Analytics with R and
Python Full Unlimited
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
applications. Building on his predictive analytics program at Northwestern, Miller covers
segmentation, target marketing, brand and product positioning, new product development, choice
modeling, recommender systems, pricing research, retail site selection, demand estimation, sales
forecasting, customer retention, and lifetime value analysis. Starting where his widely-praised
Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights
that were previously segregated in texts on web analytics, network science, information
technology, and programming. Coverage includes: The role of analytics in delivering effective
messages on the web Understanding the web by understanding its hidden structures Being
recognized on the web -- and watching your own competitors Visualizing networks and
understanding communities within them Measuring sentiment and making recommendations
Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics,
regression/classification, machine learning, and text analytics Six complete case studies address
exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-
relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data,
and more. This text's extensive set of web and network problems draw on rich public-domain data
sources; many are accompanied by solutions in Python and/or R.

3. Details Product
Author : Thomas W. Miller Jr.
●
Pages : 480 pages
●
Publisher : Pearson FT Press
●
Language :
●
ISBN-10 : 0133886557
●
ISBN-13 : 9780133886559
●

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EPUB Download Marketing Data Science: Modeling Techniques in Predictive Analytics with R
and Python Full Unlimited Link Download

6. Synopsis
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
applications. Building on his predictive analytics program at Northwestern, Miller covers
segmentation, target marketing, brand and product positioning, new product development, choice
modeling, recommender systems, pricing research, retail site selection, demand estimation, sales
forecasting, customer retention, and lifetime value analysis. Starting where his widely-praised
Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights
that were previously segregated in texts on web analytics, network science, information
technology, and programming. Coverage includes: The role of analytics in delivering effective
messages on the web Understanding the web by understanding its hidden structures Being
recognized on the web -- and watching your own competitors Visualizing networks and
understanding communities within them Measuring sentiment and making recommendations
Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics,
regression/classification, machine learning, and text analytics Six complete case studies address
exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-
relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data,
and more. This text's extensive set of web and network problems draw on rich public-domain data
sources; many are accompanied by solutions in Python and/or R.
EPUB Download Marketing Data Science: Modeling Techniques in Predictive Analytics with R and
Python Full Unlimited
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