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  ▼a  019365457
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  ▼a  1091659201
  ▼a  1096523152

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  ▼a  178934882X

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  ▼a  9781789348828
  ▼q  (electronic bk.)

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  ▼z  9781789346343

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  ▼a  2094760
  ▼b  (N$T)

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  ▼a  (OCoLC)1100643331
  ▼z  (OCoLC)1091659201
  ▼z  (OCoLC)1096523152

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  ▼a  CL0501000047
  ▼b  Safari Books Online

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  ▼a  UMI
  ▼b  eng
  ▼e  rda
  ▼e  pn
  ▼c  UMI
  ▼d  TEFOD
  ▼d  EBLCP
  ▼d  UKAHL
  ▼d  MERUC
  ▼d  UKMGB
  ▼d  OCLCF
  ▼d  YDX
  ▼d  OCLCQ
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  HF5415.125

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  ▼a  658.834
  ▼2  23

• 100

  ▼a  Hwang, Yoon Hyup,
  ▼e  author.

• 245

  ▼a  Hands-on data science for marketing:
  ▼b  improve your marketing strategies with machine learning using Python and R /:
  ▼c  Yoon Hyup Hwang.

• 260

  ▼a  Birmingham, UK:
  ▼b  Packt Publishing,
  ▼c  2019.

• 300

  ▼a  1 online resource:
  ▼b  illustrations.

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 505

  ▼a  Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Data Science and Marketing; Technical requirements; Trends in marketing; Applications of data science in marketing; Descriptive versus explanatory versus predictive analyses; Types of learning algorithms; Data science workflow; Setting up the Python environment; Installing the Anaconda distribution; A simple logistic regression model in Python; Setting up the R environment; Installing R and RStudio; A simple logistic regression model in R

• 505

  ▼a  Chapter 3: Drivers behind Marketing EngagementUsing regression analysis for explanatory analysis; Explanatory analysis and regression analysis; Logistic regression; Regression analysis with Python; Data analysis and visualizations; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables; Combining continuous and categorical variables; Regression analysis with R; Data analysis and visualization; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables

• 505

  ▼a  Combining continuous and categorical variablesSummary; Chapter 4: From Engagement to Conversion; Decision trees; Logistic regression versus decision trees; Growing decision trees; Decision trees and interpretations with Python; Data analysis and visualization; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balances by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding months; Encoding jobs; Encoding marital; Encoding the housing and loan variables; Building decision trees; Interpreting decision trees

• 505

  ▼a  Decision trees and interpretations with RData analysis and visualizations; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balance by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding the month; Encoding the job, housing, and marital variables; Building decision trees; Interpreting decision trees; Summary; Section 3: Product Visibility and Marketing; Chapter 5: Product Analytics; The importance of product analytics; Product analytics using Python; Time series trends; Repeat customers; Trending items over time

• 520

  ▼a  Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary

• 520

  ▼a  This book will be an excellent resource for both Python and R developers and will help them apply data science and machine learning to marketing with real-world data sets. By the end of this book, you will be well equipped with the required knowledge and expertise to draw insights from data and improve your marketing strategies.

• 588

  ▼a  Online resource; title from title page (Safari, viewed May 1, 2019).

• 590

  ▼a  Added to collection customer.56279.3

• 650

  ▼a  Marketing
  ▼x  Data processing.

• 650

  ▼a  Machine learning.

• 650

  ▼a  Marketing research.

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  R (Computer program language)

• 650

  ▼a  Machine learning.
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  Marketing
  ▼x  Data processing.
  ▼2  fast
  ▼0  (OCoLC)fst01010187

• 650

  ▼a  Marketing research.
  ▼2  fast
  ▼0  (OCoLC)fst01010284

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 650

  ▼a  R (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01086207

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Hwang, Yoon Hyup.
  ▼t  Hands-On Data Science for Marketing : Improve Your Marketing Strategies with Machine Learning Using Python and R.
  ▼d  Birmingham : Packt Publishing Ltd, ©2019,
  ▼z  9781789346343

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2094760

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH36147896

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  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL5744478

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  16142469

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  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2094760

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  ▼a  강리원

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  ▼a  eBook

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  ▼a  92
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