Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

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Today , a innovator of Northwestern University's widely-praised Modeling Methods in Predictive Analytics still left off, this individual integrates crucial details and insights which were previously segregated in texts on internet analytics, network science, it, and programming. Composing for both managers and college students, Thomas W. Miller clarifies essential concepts, concepts, and theory in the context of real-globe applications.

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Building on Miller's extensive group of internet and network problems pull on rich public-domain data resources;

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Starting where Miller's prestigious analytics system presents a fully-integrated treatment of both business and academic components of advertising applications in predictive analytics. This textual content' identifying legally-relevant details for lawsuit discovery;

Advertising Data Technology will be a great resource for all learners, faculty, and professional online marketers who want to make use of business analytics to boost marketing performance. Coverage contains:

• The function of analytics in providing effective messages on the internet
• Understanding the net by understanding its concealed structures
• Being regarded on the internet – and watching your personal competition
• Visualizing systems and understanding communities within them
• Measuring sentiment and making suggestions
• Leveraging key data technology methods: databases/data planning, classical/Bayesian stats, regression/classification, machine learning, and textual content analytics

Six complete case research address exceptionally relevant problems such as for example: separating genuine email from spam;s pioneering plan, Marketing Data Technology thoroughly addresses segmentation, focus on marketing, brand and item positioning, new product advancement, choice modeling, recommender systems, pricing study, retail site selection, demand estimation, sales forecasting, client retention, and lifetime worth analysis. most are accompanied by solutions in Python and/or R. gleaning insights from anonymous internet surfing data, and even more.

See also

• Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How (FT Press Analytics)
• Optimization in Operations Research (2nd Edition)
• Student Solutions Manual for Winston's Operations Research: Applications and Algorithms, 4th