The Resource Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst, (electronic resource)

Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst, (electronic resource)

Label
Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst
Title
Applied Predictive Analytics
Title remainder
Principles and Techniques for the Professional Data Analyst
Creator
Subject
Genre
Language
eng
Summary
Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful p
Cataloging source
EBLCP
http://library.link/vocab/creatorName
Abbott, Dean
Dewey number
  • 006.3
  • 006.312
Index
no index present
LC call number
QA76.9.D343
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Business
  • Business planning
  • Data mining
  • Business -- Computer programs
  • Business planning
  • Data mining
  • COMPUTERS / General
Label
Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst, (electronic resource)
Link
http://calgary.lib.overdrive.com/ContentDetails.htm?ID=FB764532-12F1-4ED0-AA93-110AFD86E5F5
Instantiates
Publication
Note
  • Description based upon print version of record
  • Ordinal Variable Transformations
Antecedent source
unknown
Color
multicolored
Contents
  • Cover; Title Page; Copyright; Contents; Chapter 1 Overview of Predictive Analytics; What Is Analytics?; What Is Predictive Analytics?; Supervised vs. Unsupervised Learning; Parametric vs. Non-Parametric Models; Business Intelligence; Predictive Analytics vs. Business Intelligence; Do Predictive Models Just State the Obvious?; Similarities between Business Intelligence and Predictive Analytics; Predictive Analytics vs. Statistics; Statistics and Analytics; Predictive Analytics and Statistics Contrasted; Predictive Analytics vs. Data Mining; Who Uses Predictive Analytics?
  • Challenges in Using Predictive AnalyticsObstacles in Management; Obstacles with Data; Obstacles with Modeling; Obstacles in Deployment; What Educational Background Is Needed to Become a Predictive Modeler?; Chapter 2 Setting Up the Problem; Predictive Analytics Processing Steps: CRISP-DM; Business Understanding; The Three-Legged Stool; Business Objectives; Defining Data for Predictive Modeling; Defining the Columns as Measures; Defining the Unit of Analysis; Which Unit of Analysis?; Defining the Target Variable; Temporal Considerations for Target Variable
  • Defining Measures of Success for Predictive ModelsSuccess Criteria for Classification; Success Criteria for Estimation; Other Customized Success Criteria; Doing Predictive Modeling Out of Order; Building Models First; Early Model Deployment; Case Study: Recovering Lapsed Donors; Overview; Business Objectives; Data for the Competition; The Target Variables; Modeling Objectives; Model Selection and Evaluation Criteria; Model Deployment; Case Study: Fraud Detection; Overview; Business Objectives; Data for the Project; The Target Variables; Modeling Objectives
  • Model Selection and Evaluation CriteriaModel Deployment; Summary; Chapter 3 Data Understanding; What the Data Looks Like; Single Variable Summaries; Mean; Standard Deviation; The Normal Distribution; Uniform Distribution; Applying Simple Statistics in Data Understanding; Skewness; Kurtosis; Rank-Ordered Statistics; Categorical Variable Assessment; Data Visualization in One Dimension; Histograms; Multiple Variable Summaries; Hidden Value in Variable Interactions: Simpson's Paradox; The Combinatorial Explosion of Interactions; Correlations; Spurious Correlations; Back to Correlations; Crosstabs
  • Data Visualization, Two or Higher DimensionsScatterplots; Anscombe's Quartet; Scatterplot Matrices; Overlaying the Target Variable in Summary; Scatterplots in More Than Two Dimensions; The Value of Statistical Significance; Pulling It All Together into a Data Audit; Summary; Chapter 4 Data Preparation; Variable Cleaning; Incorrect Values; Consistency in Data Formats; Outliers; Multidimensional Outliers; Missing Values; Fixing Missing Data; Feature Creation; Simple Variable Transformations; Fixing Skew; Binning Continuous Variables; Numeric Variable Scaling; Nominal Variable Transformation
Control code
ocn875820295
Dimensions
unknown
Extent
1 online resource (453 p.)
File format
unknown
Form of item
online
Isbn
9781118727690
Isbn Type
(electronic bk.)
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Stock number
FB764532-12F1-4ED0-AA93-110AFD86E5F5
System control number
(OCoLC)875820295
Label
Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst, (electronic resource)
Link
http://calgary.lib.overdrive.com/ContentDetails.htm?ID=FB764532-12F1-4ED0-AA93-110AFD86E5F5
Publication
Note
  • Description based upon print version of record
  • Ordinal Variable Transformations
Antecedent source
unknown
Color
multicolored
Contents
  • Cover; Title Page; Copyright; Contents; Chapter 1 Overview of Predictive Analytics; What Is Analytics?; What Is Predictive Analytics?; Supervised vs. Unsupervised Learning; Parametric vs. Non-Parametric Models; Business Intelligence; Predictive Analytics vs. Business Intelligence; Do Predictive Models Just State the Obvious?; Similarities between Business Intelligence and Predictive Analytics; Predictive Analytics vs. Statistics; Statistics and Analytics; Predictive Analytics and Statistics Contrasted; Predictive Analytics vs. Data Mining; Who Uses Predictive Analytics?
  • Challenges in Using Predictive AnalyticsObstacles in Management; Obstacles with Data; Obstacles with Modeling; Obstacles in Deployment; What Educational Background Is Needed to Become a Predictive Modeler?; Chapter 2 Setting Up the Problem; Predictive Analytics Processing Steps: CRISP-DM; Business Understanding; The Three-Legged Stool; Business Objectives; Defining Data for Predictive Modeling; Defining the Columns as Measures; Defining the Unit of Analysis; Which Unit of Analysis?; Defining the Target Variable; Temporal Considerations for Target Variable
  • Defining Measures of Success for Predictive ModelsSuccess Criteria for Classification; Success Criteria for Estimation; Other Customized Success Criteria; Doing Predictive Modeling Out of Order; Building Models First; Early Model Deployment; Case Study: Recovering Lapsed Donors; Overview; Business Objectives; Data for the Competition; The Target Variables; Modeling Objectives; Model Selection and Evaluation Criteria; Model Deployment; Case Study: Fraud Detection; Overview; Business Objectives; Data for the Project; The Target Variables; Modeling Objectives
  • Model Selection and Evaluation CriteriaModel Deployment; Summary; Chapter 3 Data Understanding; What the Data Looks Like; Single Variable Summaries; Mean; Standard Deviation; The Normal Distribution; Uniform Distribution; Applying Simple Statistics in Data Understanding; Skewness; Kurtosis; Rank-Ordered Statistics; Categorical Variable Assessment; Data Visualization in One Dimension; Histograms; Multiple Variable Summaries; Hidden Value in Variable Interactions: Simpson's Paradox; The Combinatorial Explosion of Interactions; Correlations; Spurious Correlations; Back to Correlations; Crosstabs
  • Data Visualization, Two or Higher DimensionsScatterplots; Anscombe's Quartet; Scatterplot Matrices; Overlaying the Target Variable in Summary; Scatterplots in More Than Two Dimensions; The Value of Statistical Significance; Pulling It All Together into a Data Audit; Summary; Chapter 4 Data Preparation; Variable Cleaning; Incorrect Values; Consistency in Data Formats; Outliers; Multidimensional Outliers; Missing Values; Fixing Missing Data; Feature Creation; Simple Variable Transformations; Fixing Skew; Binning Continuous Variables; Numeric Variable Scaling; Nominal Variable Transformation
Control code
ocn875820295
Dimensions
unknown
Extent
1 online resource (453 p.)
File format
unknown
Form of item
online
Isbn
9781118727690
Isbn Type
(electronic bk.)
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Stock number
FB764532-12F1-4ED0-AA93-110AFD86E5F5
System control number
(OCoLC)875820295

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