Index

A

• analytics / Analytics, predictive analytics, and data visualization
• association analysis
  • about / Association analysis
• associative logic
  • about / Associative logic
  • working / Associative logic
• Attribute-Relation File Format (ARFF) / Loading data

B

• bar chart
  • creating / Creating a bar chart
  • personalizing / Creating a bar chart
• Bike Sharing Dataset
  • reference / Understanding the bike rental problem
• bike sharing system
  • bike rental problem / Understanding the bike rental problem
  • data, exploring with Qlik Sense / Exploring the data with Qlik Sense
• binning
  • about / Binning
• boosting method / Boosting
• Business Intelligence (BI)
  • about / In-memory analysis

C

• casual users / Understanding the bike rental problem
• categorical variable
  • about / Datasets, observations, and variables
• categorical variables
  • about / Categorical variables
  • bar chart / Bar plots
  • mosaic plot / Mosaic plots
• centroid / Centroid-based clustering the using K-means algorithm
• charts
  • creating / Creating charts
  • pie chart, creating / Creating charts
  • bar chart, creating / Creating charts
  • Data menu / The Data menu
  • Sorting menu / The Sorting menu
  • Add-ons menu / The Add-ons menu
  • Appearance menu / The Appearance menu
• classifiers performance
  • measuring / Measuring the performance of classifiers
  • confusion matrix / Confusion matrix, accuracy, sensitivity, and specificity
  • accuracy / Confusion matrix, accuracy, sensitivity, and specificity
  • sensitivity / Confusion matrix, accuracy, sensitivity, and specificity
  • specificity / Confusion matrix, accuracy, sensitivity, and specificity
  • types of predictions / Confusion matrix, accuracy, sensitivity, and specificity
  • Risk Chart, obtaining / Risk Chart
  • ROC Curve / ROC Curve
• cleanup options
  • about / Cleaning up
• cluster analysis
  • about / Cluster analysis
  • centroid-based clustering, K-means algorithm used / Centroid-based clustering the using K-means algorithm
  • customer segmentation, with K-means clustering / Customer segmentation with K-means clustering
  • data, preparing in Qlik Sense / Preparing the data in Qlik Sense
  • customer segmentation sheet, creating in Qlik Sense / Creating a customer segmentation sheet in Qlik Sense
• Comma Separated Value (CSV) / Loading data
• Complexity Parameter (CP) / Cross-validation
• Comprehensive R Archive Network (CRAN) / Downloading and installing R
• correlation, among input variables
  • about / Correlations among input variables
• correlation analysis, with Rattle / Correlation Analysis with Rattle
• correlation coefficient
  • about / Correlations among input variables
• credit risks
  • classifying, with Decision Tree / Using a Decision Tree to classify credit risks
• cross-validation
  • about / Cross-validation
  • implementing / Cross-validation
• CSV file
  • loading / Loading a CSV File
• customer buying behavior / Customer segmentation and customer buying behavior
• customer segmentations
  • about / Customer segmentation and customer buying behavior
  • types / Customer segmentation and customer buying behavior

D

• DAR methodology
  • reference link / Further learning
• Dashboard Analysis and Reporting (DAR)
  • about / In-memory analysis, The DAR approach
• dashboards
  • about / Data analysis, data applications, and dashboards, Data applications and dashboards
• data
  • loading / Loading data, Loading data and creating a data model
  • rescaling / Rescaling data
  • Impute option, used for dealing with missing values / Using the Impute option to deal with missing values
  • exporting / Exporting data
  • preparing / Preparing the data
  • analyzing / Analyzing your data
• data analysis, Qlik Sense
  • about / Qlik Sense data analysis
  • in memory analysis / In-memory analysis
  • associative logic / Associative experience
• data applications
  • about / Data analysis, data applications, and dashboards, Data applications and dashboards
• data exploring, with Qlik Sense
  • about / Exploring the data with Qlik Sense
  • temporal patterns, checking / Checking for temporal patterns
  • visual correlation analysis / Visual correlation analysis
• data model
  • creating / Loading data and creating a data model, Preparing the data
  • checking / Preparing the data
• Data Science
  • reference URL / Further learning
• data science / Datasets, observations, and variables
• dataset
  • about / Datasets, observations, and variables
  • variable description / Datasets, observations, and variables
  • reference / Customer segmentation with K-means clustering
  • instant / Understanding the bike rental problem
  • dteday / Understanding the bike rental problem
  • season / Understanding the bike rental problem
  • yr / Understanding the bike rental problem
  • mnth / Understanding the bike rental problem
  • hr / Understanding the bike rental problem
  • weekday / Understanding the bike rental problem
  • workingday / Understanding the bike rental problem
  • weathersit / Understanding the bike rental problem
  • temp / Understanding the bike rental problem
  • atemp / Understanding the bike rental problem
  • hum / Understanding the bike rental problem
  • windspeed / Understanding the bike rental problem
  • casual / Understanding the bike rental problem
  • registered / Understanding the bike rental problem
  • cnt / Understanding the bike rental problem
• datasets
  • partitioning / Partitioning datasets and model optimization
• data storytelling, Qlik Sense
  • about / Data storytelling with Qlik Sense
  • audience / Data storytelling with Qlik Sense
  • objective / Data storytelling with Qlik Sense
  • key messages / Data storytelling with Qlik Sense
  • story / Data storytelling with Qlik Sense
  • links / Data storytelling with Qlik Sense
  • reviewing / Data storytelling with Qlik Sense
  • new story, creating / Creating a new story
• data transformation
  • about / Transforming data
  • Rattle, used / Transforming data with Rattle
  • variables, recoding / Recoding variables
  • binning / Binning
  • indicator variables / Indicator variables
• data visualization / Analytics, predictive analytics, and data visualization
  • books, for references / Further learning
• data visualization, Qlik Sense
  • about / Data visualization in Qlik Sense
  • visualization toolbox / Visualization toolbox
  • bar chart, creating / Creating a bar chart
• Decision Tree
  • creating / Entropy and information gain
  • using, for credit risk classification / Using a Decision Tree to classify credit risks
  • URL / Using a Decision Tree to classify credit risks
  • loan applications, scoring with Rattle / Using Rattle to score new loan applications
  • Qlik Sense application, creating / Creating a Qlik Sense application to predict credit risks
• Decision Tree Learning
  • about / Decision Tree Learning
  • advantages / Decision Tree Learning
  • disadvantages / Decision Tree Learning
• Default? Attribute / Confusion matrix, accuracy, sensitivity, and specificity
• default charts, Qlik Sense
  • Bar chart / Visualization toolbox
  • Combo chart / Visualization toolbox
  • Filter pane / Visualization toolbox
  • Line chart / Visualization toolbox
  • Map / Visualization toolbox
  • Pie chart / Visualization toolbox
  • Scatter plot / Visualization toolbox
  • Table / Visualization toolbox
  • Pivot Table / Visualization toolbox
  • Text & image / Visualization toolbox
  • Treemap / Visualization toolbox
  • Extensions / Visualization toolbox
• dendrogram
  • about / Hierarchical clustering
• descriptive analytics
  • about / Machine learning – unsupervised and supervised learning
• disadvantages, Decision Tree Learning
  • unstable / Decision Tree Learning
  • overfitting / Decision Tree Learning
• distributions
  • visualizing / Visualizing distributions
  • numeric variables / Numeric variables
  • categorical variables / Categorical variables

E

• Ensemble methods
  • about / Ensemble classifiers
  • URL / Ensemble classifiers
  • boosting / Boosting
  • Random Forest / Random Forest
  • Supported Vector Machine (SVM) / Supported Vector Machines
• entropy
  • about / Entropy and information gain
• environment
  • installing / Installing the environment
• error rate / Confusion matrix, accuracy, sensitivity, and specificity
• Explore Missing option
  • about / The Explore Missing and Hierarchical options

F

• fact table
  • about / Associative experience

G

• General Public License (GNU) / Introducing R, Rattle, and Qlik Sense Desktop
• Graphical User Interface (GUI) / Introducing R, Rattle, and Qlik Sense Desktop

H

• hierarchical clustering
  • about / Hierarchical clustering
• Hierarchical option
  • about / The Explore Missing and Hierarchical options

I

• indicator variables
  • about / Indicator variables
  • Join Categories option / Join Categories
  • As Category option / As Category
  • As Numeric option / As Numeric
• information gain
  • about / Entropy and information gain
• input variables
  • about / Datasets, observations, and variables

K

• Kaggle
  • about / Datasets, observations, and variables
  • URL / Datasets, observations, and variables, Regression performance
• Key Performance Indicator (KPI)
  • about / Data analysis, data applications, and dashboards
• Key Performance Indicators (KPI) / Exploring Qlik Sense Desktop
• kurtosis
  • about / Measures of the shape of the distribution – skewness and kurtosis
  • URL / Measures of the shape of the distribution – skewness and kurtosis

L

• labeled dataset
  • about / Machine learning – unsupervised and supervised learning
• Logistic Regression / Linear and Logistic Regression
• Lower Confidence Level / Measures of the shape of the distribution – skewness and kurtosis

M

• Machine Learning (ML)
  • about / Machine learning – unsupervised and supervised learning
  • supervised learning / Machine learning – unsupervised and supervised learning
  • unsupervised learning / Machine learning – unsupervised and supervised learning
  • cluster analysis / Cluster analysis
  • hierarchical clustering / Hierarchical clustering
  • association analysis / Association analysis
• measures of central tendency
  • mean / Measures of central tendency – mean, median, and mode
  • median / Measures of central tendency – mean, median, and mode
  • mode / Measures of central tendency – mean, median, and mode
• measures of dispersion
  • about / Measures of dispersion – range, quartiles, variance, and standard deviation
  • range / Range
  • quartiles / Quartiles
  • variance / Variance
  • standard deviation / Standard deviation
• menus, charts
  • Data menu / The Data menu
  • Sorting menu / The Sorting menu
  • Add-ons menu / The Add-ons menu
  • Appearance menu / The Appearance menu
• model evaluation
  • about / Model evaluation
  • performing / Model evaluation
  • new data, scoring / Scoring new data
• model optimization / Partitioning datasets and model optimization
• models
  • Linear Regression / Linear and Logistic Regression
  • Logistic Regression / Linear and Logistic Regression
  • Neural Networks / Neural Networks
• MOOC course
  • URL / Further learning
• Multiple Linear Regression / Linear and Logistic Regression

N

• Neural Network model
  • about / Neural Networks
  • input layer / Neural Networks
  • hidden layer / Neural Networks
  • output layer / Neural Networks
• nominal categorical variable
  • about / Datasets, observations, and variables
• numeric variable
  • about / Datasets, observations, and variables
• numeric variables
  • about / Numeric variables
  • Box Plot / Box plots
  • histogram / Histograms
  • cumulative plot / Cumulative plots

O

• Open Database Connectivity (ODBC) / Loading data
• ordinal categorical variable
  • about / Datasets, observations, and variables
• output variables
  • about / Datasets, observations, and variables
• overfitting / Underfitting and overfitting

P

• predictive analytics / Analytics, predictive analytics, and data visualization
  • about / Machine learning – unsupervised and supervised learning
• predictive analytics process
  • steps / Analytics, predictive analytics, and data visualization

Q

• Qlik
  • about / Visualization toolbox
• Qlik Branch
  • URL / Visualization toolbox
• Qlik Community
  • URL / Visualization toolbox
• Qlik home page
  • URL / Installing Qlik Sense Desktop
• Qlik Market
  • URL / Visualization toolbox
• Qlik Sense
  • data visualization / Data visualization in Qlik Sense
  • default charts / Visualization toolbox
  • data analysis / Qlik Sense data analysis
  • data storytelling / Data storytelling with Qlik Sense
  • about / Scoring new data
  • references / Further learning
• Qlik Sense application
  • creating, for predicting credit risks / Creating a Qlik Sense application to predict credit risks
  • creating / Creating a Qlik Sense App to control the activity
• Qlik Sense Desktop
  • ways of using / Purpose of the book
  • about / Introducing R, Rattle, and Qlik Sense Desktop
  • installing / Installing Qlik Sense Desktop
  • exploring / Exploring Qlik Sense Desktop
  • URL / Further learning
• Qlik Sense Desktop Tutorials
  • about / Visualization toolbox
• quartiles
  • about / Quartiles
  • URL / Quartiles

R

• R
  • about / Introducing R, Rattle, and Qlik Sense Desktop, Scoring new data
  • downloading / Downloading and installing R
  • installing / Downloading and installing R
  • installation, testing with R Console / Starting the R Console to test your R installation
• R-Square / Predicted versus Observed Plot
• Random Forest / Random Forest
• range
  • about / Range
• Rattle
  • about / Introducing R, Rattle, and Qlik Sense Desktop, Scoring new data
  • downloading / Downloading and installing Rattle
  • installing / Downloading and installing Rattle
  • used, for scoring loan applications / Using Rattle to score new loan applications
  • models / Other models
• Rattle, using, for forecast
  • about / Using Rattle to forecast the demand
  • correlation analysis / Correlation Analysis with Rattle
  • model, creating / Building a model
  • performance, improving / Improving performance
• R Console
  • starting, for testing R installation / Starting the R Console to test your R installation
• registered users / Understanding the bike rental problem
• regression performance
  • measuring / Regression performance
  • predicted, versus observed plot / Predicted versus Observed Plot
• rescaling
  • about / Rescaling data
  • data / Rescaling data
• Risk Chart
  • about / Risk Chart
  • obtaining / Risk Chart
• ROC Curve
  • about / ROC Curve
• roles, variable
  • input / Loading data
  • target / Loading data
  • risk / Loading data
  • identifier / Loading data
  • ident / Loading data
  • Ignore / Loading data

S

• simple data app
  • creating / Creating a simple data app
• Simple Linear Regression / Linear and Logistic Regression
• skewness
  • about / Measures of the shape of the distribution – skewness and kurtosis
  • URL / Measures of the shape of the distribution – skewness and kurtosis
• standard deviation
  • about / Standard deviation
• Standard Error / Measures of the shape of the distribution – skewness and kurtosis
• summary reports
  • about / Summary reports
  • measures of central tendency / Measures of central tendency – mean, median, and mode
  • measures of dispersion / Measures of dispersion – range, quartiles, variance, and standard deviation
  • measures of shape of distribution / Measures of the shape of the distribution – skewness and kurtosis
• supervised learning
  • about / Machine learning – unsupervised and supervised learning
• Supported Vector Machine (SVM) / Supported Vector Machines

T

• target variables
  • about / Datasets, observations, and variables
• text summaries
  • about / Text summaries
  • summary reports / Summary reports
  • missing values, displaying / Showing missing values
• training dataset
  • about / Machine learning – unsupervised and supervised learning
• types of predictions, classifiers performance
  • True Positive / Confusion matrix, accuracy, sensitivity, and specificity
  • False Positive / Confusion matrix, accuracy, sensitivity, and specificity
  • True Negative / Confusion matrix, accuracy, sensitivity, and specificity
  • False Negative / Confusion matrix, accuracy, sensitivity, and specificity

U

• UCI Machine Learning Repository
  • reference / Regression performance
• underfitting / Underfitting and overfitting
• unlabeled dataset
  • about / Association analysis
• unlabeled datasets
  • about / Machine learning – unsupervised and supervised learning
• unsupervised learning
  • about / Machine learning – unsupervised and supervised learning
• Upper Confidence Level / Measures of the shape of the distribution – skewness and kurtosis
• user groups
  • executive management / Data analysis, data applications, and dashboards
  • middle managers / Data analysis, data applications, and dashboards
  • analysts / Data analysis, data applications, and dashboards

V

• variable
  • about / Loading data
• variance
  • about / Variance
• visualization toolbox
  • about / Visualization toolbox

W

• Weka / Loading data