دانلود کتاب Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python

Now, a leader 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 Miller's pioneering program,
Marketing Data Science thoroughly addresses
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 Miller's 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.Marketing Data Science will be an invaluable resource
for all students, faculty, and professional marketers who want to
use business analytics to improve marketing performance.