Tabla de Contenidos: “…Installing Advanced Analytics Integration with R -- Installing Advanced Analytics Integration with Python -- Setting up Qlik AutoML -- Cloud integrations with REST -- General Advanced Analytics connector -- Amazon SageMaker connector -- Azure ML connector -- Qlik AutoML connector -- Summary -- Chapter 6: Preprocessing and Exploring Data with Qlik Sense -- Creating a data model with the data manager -- Introduction to the data manager -- Introduction to Qlik script -- Important functions in Qlik script -- Validating data -- Data lineage and data catalogs -- Data lineage -- Data catalogs -- Exploring data and finding insights -- Summary -- Chapter 7: Deploying and Monitoring Machine Learning Models -- Building a model in an on-premises environment using the Advanced Analytics connection -- Monitoring and debugging models -- Summary -- Chapter 8: Utilizing Qlik AutoML -- Features of Qlik AutoML -- Using Qlik AutoML in a cloud environment -- Creating and monitoring a machine learning model with Qlik AutoML -- Connecting Qlik AutoML to an on-premises environment -- Best practices with Qlik AutoML -- Summary -- Chapter 9: Advanced Data Visualization Techniques for Machine Learning Solutions -- Visualizing machine learning data -- Chart and visualization types in Qlik -- Bar charts -- Box plots -- Bullet charts -- Distribution plots -- Histogram -- Maps -- Scatter plots -- Waterfall charts -- Choosing visualization type -- Summary -- Part 3: Case studies and best practices -- Chapter 10: Examples and Case Studies -- Linear regression example -- Customer churn example -- Summary -- Chapter 11: Future Direction -- The future trends of machine learning and AI -- How to recognize potential megatrends -- Summary -- Index -- About Packt -- Other Books You May Enjoy…”

Tabla de Contenidos: “….; Variables nominales: la proporción; Medidas no centrales; Los cuartiles; Los percentiles; La forma de la distribución; Box-plots; Medidas de dispersión…”

Tabla de Contenidos: “…Polar Charts -- Summary -- Chapter 10: Working with Colors -- pcolor() -- pcolormesh() -- colorbar() -- Summary -- Chapter 11: 3D Visualizations in Matplotlib -- Getting Ready -- Plotting 3D Lines -- 3D Scatter Plots -- 3D Contours -- Wireframes, Surfaces, and Sample Data -- Bar Graphs -- Quiver and Stem Plots -- 3D Volumes -- Summary -- Chapter 12: Animations with Matplotlib -- Animation Basics -- Celluloid Library -- Summary -- Chapter 13: More Visualizations with Matplotlib -- Visualizing a Function as an Image and a Contour -- 3D Vignettes -- Decorated Scatter Plots -- Time Plots and Signals -- Filled Plots -- Step Plots -- Hexbins -- XKCD Style -- Summary -- Chapter 14: Introduction to Pandas -- Introduction to Pandas -- Series in Pandas -- Basic Operations on Series -- Dataframe in Pandas -- Summary -- Chapter 15: Data Acquisition -- Plain-Text File Handling -- Handling CSV Files with Python -- Python and Excel -- Writing and Reading Files with NumPy -- Reading the Data from a CSV File with NumPy -- Matplotlib CBook -- Reading Data from a CSV -- Reading Data from an Excel File -- Reading Data from JSON -- Reading Data from a Pickle File -- Reading Data from the Web -- Interacting with the Web API -- Reading Data from a Relational Database Table -- Reading Data from the Clipboard -- Summary -- Chapter 16: Visualizing Data with Pandas and Matplotlib -- Simple Plots -- Bar Graphs -- Histograms -- Box Plots -- Area Plots -- Scatter Plots -- Hexagonal Bin Plots -- Pie Charts -- Summary -- Chapter 17: Introduction to Data Visualization with Seaborn -- What Is Seaborn? …”

Tabla de Contenidos: “…Data Exploration -- Computing statistics -- Overview of visualizations -- Visual guide for the views -- Distance matrix -- Using visual properties -- Color -- Size -- Shape -- KNIME views -- HiLite -- Use cases for HiLite -- Row IDs -- Extreme values -- Basic KNIME views -- The Box plots -- Hierarchical clustering -- Histograms -- Interactive Table -- The Lift chart -- Lines -- Pie charts -- The Scatter plots -- Spark Line Appender -- Radar Plot Appender -- The Scorer views -- JFreeChart -- The Bar charts -- The Bubble chart -- Heatmap -- The Histogram chart -- The Interval chart -- The Line chart -- The Pie chart -- The Scatter plot -- Open Street Map -- 3D Scatterplot -- Other visualization nodes -- The R plot, Python plot, and Matlab plot -- The official R plots -- The RapidMiner view -- The HiTS visualization -- Tips for HiLiting -- Using Interactive HiLite Collector -- Finding connections -- Visualizing models -- Further ideas -- Summary -- 4. …”

Tabla de Contenidos: “…Using Scaled Symbols -- Editing the Chart -- Parts of a Whole -- Pie Chart -- Making a Pie Chart -- Donut Chart -- Making a Donut Chart -- Square Pie -- Making a Square Pie Chart -- Treemap -- Making a Treemap -- Editing the Chart -- Rank and Order -- Categories and Time -- Stacked Bar Chart -- Making a Stacked Bar Chart -- Formatting the Data -- Making the Chart -- Stacked Area Chart -- Making a Stacked Area Chart -- Alluvial Diagram -- Making an Alluvial Diagram -- Bump Chart -- Wrapping Up -- Chapter 6 Visualizing Relationships -- Correlation -- Scatterplot -- Making a Scatterplot -- Bubble Plot -- Making a Bubble Plot -- Differences -- Barbell Chart -- Making a Barbell Chart -- Difference Chart -- Making a Difference Chart -- Highlighting Differences -- Multiple Variables -- Heatmap for Multiple Variables -- Making a Heatmap for Multiple Variables -- Parallel Coordinates -- Making Parallel Coordinates -- Separating Views -- Connections -- Network Graph -- Making a Network Graph -- Wrapping Up -- Chapter 7 Visualizing Space -- Working with Spatial Data -- Geocoding Addresses -- Map Projections -- Locations -- Points -- Mapping Points -- Scaled Symbols -- Adding Scaled Symbols -- Lines -- Adding Lines -- Spatial Distributions -- Choropleth Map -- Making a Choropleth Map -- Cartogram -- Making a Cartogram -- Dot Density Map -- Making a Dot Density Map -- Space and Time -- Sequence of Maps -- Animated Map -- Making an Animated Map -- Wrapping Up -- Chapter 8 Analyzing Data Visually -- Gathering Information -- Overviews -- Summaries -- Making a Box Plot -- Distributions -- Quality of the Data -- Adjusting Questions -- Exploring Details -- Comparisons -- Patterns -- Uncertainty -- Outliers -- Drawing Conclusions -- Wrapping Up -- Chapter 9 Designing with Purpose -- Good Visualization -- Infinite Options -- Visualization Components -- Insight for Others…”

Tabla de Contenidos: “…Data visualization -- Bar chart -- Multiple bar charts -- Box plot -- Pie chart -- Bubble chart -- Summary -- Index…”

Tabla de Contenidos: “…Outlier detection is an important aspect of data analysis as it helps determine if the data is correct, looks at the skewness of the data, and removes any unexpected -- Chapter 13: Outlier Detection -- Measures of central tendency and dispersion -- Case scenario -- Key KPIs -- Methods for detecting outliers -- Box plot method -- Handling outliers -- Case scenario -- Key points to keep in mind while handling outliers -- Applying outlier detection -- Challenges and limitations -- Best practices -- Summary -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…-- BI roles -- Problem solving -- Specific industry knowledge and subject matter expertise -- Communication skills -- Statistical analysis -- Technical knowledge -- Business acumen -- Keep up with innovation -- Potential entrances to the BI world -- BI roadmap -- ETL developers -- Data architects -- Data modelers -- BI developers -- Data scientists -- Technology solutions stack -- Non-technical data analysis -- Case 1 -- Case 2 -- Case 3 -- Case 4 -- Case 5 -- Summary -- Chapter 2: How to Become Proficient in Analyzing Data -- Building a taxonomy of your data sources -- How to use a BI tool to explore data -- Understanding your data needs -- Summary -- Chapter 3: How to Talk Data -- Presenting data -- Know your audience -- Choose the right visualization -- Keep it simple -- Use color and formatting effectively -- Provide context -- Tell a story -- High-level dashboards -- Operational reports -- Talking to stakeholders -- Data visualization taxonomy -- Bar charts -- Line charts -- Pie charts -- Scatter plots -- Area charts -- Heat maps -- Bubble charts -- Gauge charts -- Tree maps -- Box plots -- Advanced data visualizations -- Sankey diagrams -- Bullet charts -- Taxonomy diagrams -- Pareto diagrams -- Decision tree for picking a visualization -- Storytelling -- Summary -- Chapter 4: How To Crack the BI Interview Process -- Finding the right interview -- Building a business problem and data solutions matrix -- Potential business cases and solutions -- Teamwork -- Customer service -- Adaptability -- Time management -- Communication -- Motivation and values -- A hypothetical BI interview process and what to expect -- General business intelligence interview questions -- Scenario-based BI questions…”

Tabla de Contenidos: “…-- How to Become Certified -- Who Should Buy This Book -- How This Book Is Organized -- Chapter Features -- Bonus Digital Contents -- Conventions Used in This Book -- Google Cloud Professional ML Engineer Objective Map -- How to Contact the Publisher -- Chapter 1 Framing ML Problems -- Translating Business Use Cases -- Machine Learning Approaches -- Supervised, Unsupervised, and Semi-supervised Learning -- Classification, Regression, Forecasting, and Clustering -- ML Success Metrics -- Regression -- Responsible AI Practices -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Exploring Data and Building Data Pipelines -- Visualization -- Box Plot -- Line Plot -- Bar Plot -- Scatterplot -- Statistics Fundamentals -- Mean -- Median -- Mode -- Outlier Detection -- Standard Deviation -- Correlation -- Data Quality and Reliability -- Data Skew -- Data Cleaning -- Scaling -- Log Scaling -- Z-score -- Clipping -- Handling Outliers -- Establishing Data Constraints -- Exploration and Validation at Big-Data Scale -- Running TFDV on Google Cloud Platform -- Organizing and Optimizing Training Datasets -- Imbalanced Data -- Data Splitting -- Data Splitting Strategy for Online Systems -- Handling Missing Data -- Data Leakage -- Summary -- Exam Essentials -- Review Questions -- Chapter 3 Feature Engineering -- Consistent Data Preprocessing -- Encoding Structured Data Types -- Mapping Numeric Values -- Mapping Categorical Values -- Feature Selection -- Class Imbalance -- Classification Threshold with Precision and Recall -- Area under the Curve (AUC)…”

Tabla de Contenidos: “…The coordinate system -- Faceting -- Theme -- Installing ggplot2 -- Scatter plots -- Histogram plots -- Density plots -- Probability plots -- dnorm() -- pnorm() -- rnorm() -- Box plots -- Residual plots -- Summary -- Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown -- Technical requirements -- Installing R Markdown -- Working with R Markdown -- Reproducible data analysis reports with knitr -- Exporting and customizing reports -- Summary -- Section 2: Univariate, Time Series, and Multivariate Data -- Chapter 6: Univariate and Control Datasets -- Technical requirements -- Reading the dataset -- Cleaning and tidying up the data -- Understanding the structure of the data -- Hypothesis tests -- Statistical hypothesis in R -- The t-test in R -- Directional hypothesis in R -- Correlation in R -- Tietjen-Moore test -- Parsimonious models -- Probability plots -- The Shapiro-Wilk test -- Summary -- Chapter 7: Time Series Datasets -- Technical requirements -- Introducing and reading the dataset -- Cleaning the dataset -- Mapping and understanding structure -- Hypothesis test -- t-test in R -- Directional hypothesis in R -- Grubbs' test and checking outliers -- Parsimonious models -- Bartlett's test -- Data visualization -- Autocorrelation plots -- Spectrum plots -- Phase plots -- Summary -- Chapter 8: Multivariate Datasets -- Technical requirements -- Introducing and reading a dataset -- Cleaning the data -- Mapping and understanding the structure -- Hypothesis test -- t-test in R -- Directional hypothesis in R -- Parsimonious model -- Levene's test -- Data visualization -- Principal Component Regression -- Partial Least Squares Regression -- Summary -- Section 3: Multifactor, Optimization, and Regression Data Problems -- Chapter 9: Multi-Factor Datasets -- Technical requirements -- Introducing and reading the dataset…”

Tabla de Contenidos: “…-- Tableau User Interface -- The Data Window -- Shelves and Cards -- Basic Tableau Design Flow -- Chapter 2: Basic Visualization Design -- Using Show Me -- Choosing Mark Types -- Color, Size, Shape, and Label Options -- Choosing Color Options -- Setting Mark Size -- Choosing Shapes -- Text Tables and Mark Labels -- Formatting Options -- Evaluating Multiple Measures -- Shared Axis Charts -- Dual Axis Charts -- Chapter 3: Data Connection Details -- Connecting to Various Data Sources -- Adding and Joining Multiple Tables from the Same Database -- Customizing Your View of the Data -- Modifying Tableau's Default Field Assignments -- Hiding, Renaming, and Combining Fields -- Changing Default Field Appearance -- Using Hierarchies, Groups, and Sets -- Saving and Sharing Metadata -- Extracting Data -- Data Blending -- Moving from Test to Production Databases -- Chapter 4: Top 10 Chart Types -- Bar Chart -- Line/Area Chart -- Tableau 8 Forecasting -- Pie Chart -- Text Table/Crosstab -- Scatter Plot -- Bubble Chart -- Bullet Graph -- Box Plot -- Tree Map -- Word Cloud -- Chapter 5: Interacting with the Viewer -- Filtering Data -- Basic Filtering -- Interactive Filtering -- Quick Filters -- Parameters -- Creating a Parameter -- Displaying a Parameter -- Using a Parameter in a Worksheet -- Worksheet Actions -- Filter Actions -- Highlight Actions -- URL Actions -- Chapter 6: Tableau Maps -- Geocoded Fields -- Geographic Hierarchies and Ambiguity -- Custom Geocoding -- Background Maps and Layers -- Map Options -- Web Map Services -- Mapping and Mark Types -- Custom Background Images -- Generating Your Own Coordinate System -- Adding a Custom Background Image…”

Tabla de Contenidos: “…-- Visualization plots -- Bar graphs and pie charts -- Bar graphs -- Pie charts -- Box plots -- Scatter plots and bubble charts -- Scatter plots -- Bubble charts -- KDE plots -- Summary -- Chapter 2: Data Analysis and Visualization -- Why does visualization require planning? …”

Tabla de Contenidos: “…Tables and graphs; 4.1 Introduction; 4.2 Tables; 4.2.1 Data tables; 4.2.2 Contingency tables; 4.2.3 Summary tables; 4.3 Graphs; 4.3.1 Overview; 4.3.2 Frequency polygrams and histograms; 4.3.3 Scatterplots; 4.3.4 Box plots…”

Tabla de Contenidos: “…-- Summary -- References -- Chapter 8: Techniques for Identifying and Removing Bias -- The bias conundrum -- Types of bias -- Easy to identify bias -- Difficult to identify bias -- The data-centric imperative -- Sampling methods -- Other data-centric techniques -- Case study -- Loading the libraries -- AllKNN undersampling method -- Instance hardness undersampling method -- Oversampling methods -- Shapley values to detect bias, oversample, and undersample data -- Summary -- Chapter 9: Dealing with Edge Cases and Rare Events in Machine Learning -- Importance of detecting rare events and edge cases in machine learning -- Statistical methods -- Z-scores -- Interquartile Range (IQR) -- Box plots -- Scatter plots -- Anomaly detection -- Unsupervised method using Isolation Forest -- Semi-supervised methods using autoencoders -- Supervised methods using SVMs -- Data augmentation and resampling techniques -- Oversampling using SMOTE -- Undersampling using RandomUnderSampler -- Cost-sensitive learning -- Choosing evaluation metrics -- Ensemble techniques -- Bagging -- Boosting -- Stacking -- Summary -- Part 4: Getting Started with Data-Centric ML -- Chapter 10: Kick-Starting Your Journey in Data-Centric Machine Learning -- Solving six common ML challenges -- Being a champion for data quality -- Bringing people together -- Taking accountability for AI ethics and fairness -- Making data everyone's business - our own experience -- Summary -- References -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…3.5.2 Benchmark Test on CEC-17 Functions -- 3.6 Analytical Validation of Proposed Variant -- 3.6.1 Convergence Rate Test -- 3.6.2 Box Plot Analysis -- 3.6.3 Wilcoxon Rank Sum Test -- 3.6.4 Scalability Test -- 3.7 Design Analysis of Harmonic Estimator -- 3.7.1 Assessment of Harmonic Estimator Design Problem 1 -- 3.7.2 Assessment of Harmonic Estimator Design Problem 2 -- 3.8 Conclusion -- References -- Chapter 4 Applications of Cuckoo Search Algorithm in Reliability Optimization -- 4.1 Introduction -- 4.2 Cuckoo Search Algorithm -- 4.2.1 Performance of Cuckoo Search Algorithm -- 4.2.2 Levy Flights -- 4.2.3 Software Reliability -- 4.3 Modified Cuckoo Search Algorithm (MCS) -- 4.4 Optimization in Module Design -- 4.5 Optimization at Dynamic Implementation -- 4.6 Comparative Study of Support of Modified Cuckoo Search Algorithm -- 4.7 Results and Discussions -- 4.8 Conclusion -- References -- Chapter 5 Series-Parallel Computer System Performance Evaluation with Human Operator Using Gumbel-Hougaard Family Copula -- 5.1 Introduction -- 5.2 Assumptions, Notations, and Description of the System -- 5.2.1 Notations -- 5.2.2 Assumptions -- 5.2.3 Description of the System -- 5.3 Reliability Formulation of Models -- 5.3.1 Solution of the Model -- 5.4 Some Particular Cases Based on Analytical Analysis of the Model -- 5.4.1 Availability Analysis -- 5.4.2 Reliability Analysis -- 5.4.3 Mean Time to Failure (MTTF) -- 5.4.4 Cost-Benefit Analysis -- 5.5 Conclusions Through Result Discussion -- References -- Chapter 6 Applications of Artificial Intelligence in Sustainable Energy Development and Utilization -- 6.1 Energy and Environment -- 6.2 Sustainable Energy -- 6.3 Artificial Intelligence in Industry 4.0 -- 6.4 Introduction to AI and its Working Mechanism -- 6.5 Biodiesel -- 6.6 Transesterification Process -- 6.7 AI in Biodiesel Applications -- 6.8 Conclusion…”

Tabla de Contenidos: “…Exploring Data -- Overview -- Inspecting Data and Its Properties -- Header or Not, Here I Come -- Inspect All the Data -- Feature Names and Data Types -- Unique Identifiers, Continuous Variables, and Factors -- Computing Descriptive Statistics -- Column Statistics -- R One-Liners on the Shell -- Creating Visualizations -- Displaying Images from the Command Line -- Plotting in a Rush -- Creating Bar Charts -- Creating Histograms -- Creating Density Plots -- Happy Little Accidents -- Creating Scatter Plots -- Creating Trend Lines -- Creating Box Plots -- Adding Labels -- Going Beyond Basic Plots -- Summary -- For Further Exploration -- Chapter 8. …”

Tabla de Contenidos: “…Colors have to be chosen carefully -- A bit of theory - chromatic circle, hue, and luminosity -- Visualizing your data with ggplot -- One more gentle introduction - the grammar of graphics -- A layered grammar of graphics - ggplot2 -- Visualizing your banking movements with ggplot2 -- Visualizing the number of movements per day of the week -- Further references -- Summary -- Chapter 3: The Data Mining Process - CRISP-DM Methodology -- The Crisp-DM methodology data mining cycle -- Business understanding -- Data understanding -- Data collection -- How to perform data collection with R -- Data import from TXT and CSV files -- Data import from different types of format already structured as tables -- Data import from unstructured sources -- Data description -- How to perform data description with R -- Data exploration -- What to use in R to perform this task -- The summary() function -- Box plot -- Histograms -- Data preparation -- Modelling -- Defining a data modeling strategy -- How similar problems were solved in the past -- Emerging techniques -- Classification of modeling problems -- How to perform data modeling with R -- Evaluation -- Clustering evaluation -- Classification evaluation -- Regression evaluation -- How to judge the adequacy of a model's performance -- What to use in R to perform this task -- Deployment -- Deployment plan development -- Maintenance plan development -- Summary -- Chapter 4: Keeping the House Clean - The Data Mining Architecture -- A general overview -- Data sources -- Types of data sources -- Unstructured data sources -- Structured data sources -- Key issues of data sources -- Databases and data warehouses -- The third wheel - the data mart -- One-level database -- Two-level database -- Three-level database -- Technologies -- SQL -- MongoDB -- Hadoop -- The data mining engine -- The interpreter…”

Tabla de Contenidos: “…-- Identifying effective and ineffective visualizations -- Scatter plots -- Line graphs -- Bar charts -- Histograms -- Box plots -- When graphs and statistics lie -- Correlation versus causation -- Simpson's paradox -- If correlation doesn't imply causation, then what does? …”

Tabla de Contenidos: “…-- Identifying effective visualizations -- Scatter plots -- Line graphs -- Bar charts -- Histograms -- Box plots -- When graphs and statistics lie -- Correlation versus causation -- Simpson's paradox -- If correlation doesn't imply causation, then what does? …”

Tabla de Contenidos: “…-- Line charts -- Scatter plots -- Box plots -- Advanced visualization technique -- Prefuse -- IVTK Graph toolkit -- Other libraries -- Summary -- Chapter 4: Basics of Machine Learning -- What is machine learning? …”