Modern Business Analytics Deanne Larson

Spis treści książki

• Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Role of Business Analyst and Analytics
  • What Is the Role of a Business Analyst?
    • Skills
    • Responsibilities
    • Types of Analysts
      • Marketing analyst
      • Financial analyst
      • Functional analyst
      • System analyst
      • Data analyst
  • Why Does a Business Analyst Need to Know Analytics?
    • Data Explosion
    • Business Context
    • Analytics
      • Descriptive
      • Diagnostic
      • Discovery
      • Predictive
      • Prescriptive
  • Business Analyst Contributing to Analytics Value
  • Business Problems Solved by Analytics
    • Collaboration with Other Teams
    • Skill Sets Used in Analytics
    • Python and R
  • Analytics Project Life Cycle
  • Summary
• 2. Methodologies for the Business Analyst and Analytics Projects
  • Business Understanding
    • Determine Business Objectives
    • Assess Situation
    • Determine Goals
    • Establish Approach and Plan
    • Assessment of Tools and Techniques
  • Data Exploration and Preparation
    • Assess Data Content and Quality
    • Select and Clean Data
    • Construct and Integrate Data
    • Produce Dataset for Model Development
  • Modeling and Evaluation
    • Select Analytics Technique
    • Build and Assess Model
  • Deployment
    • Assess Model Performance
    • Determine Assessment Intervals
  • Model Operations
    • Monitoring Models
    • Life of a Model
    • Retraining
  • Summary
• 3. Introduction to R and Python
  • R and Python Installation and Setup Options
    • Why Learn R and Python?
    • Learning Both at Once Versus One at a Time
    • Pros and Cons of Different Learning Strategies
    • R Installation
    • Python Installation
  • R and Python Scripting
    • R Language Scripting
    • Python Language Scripting
  • Object-Oriented Concepts
    • Structure of OOP
      • Class
      • Object
      • Methods and attributes
    • Principles of OOP
      • Encapsulation
      • Abstraction
      • Inheritance
      • Polymorphism
  • R and Python Data Types
    • R Data Types
    • R Structures
    • Python Data Types
    • Python Data Structures
  • Interaction with Relational Databases
    • Why Relational Databases?
    • R Connection to Relational Databases
    • Examples of R and Relational Databases
    • SQLite
    • Python Connection to Relational Databases
    • Examples of Python and Relational Databases
  • Summary
• 4. Statistical Analysis with R and Python
  • Example Analytical Projects
    • Telecom Churn
    • A/B Testing
    • Marketing Campaigns
    • Financial Forecasting
    • Healthcare Diagnosis
  • Starting with the Problem Statement
  • Getting to the Analytical Problem
    • Classification
    • Regression
    • What Do We Want to Measure?
    • Analysis Approaches
  • EDA
    • Unsupervised Learning
    • Statistical Analysis for Regression
    • Analysis for Classification
    • Role of Hypothesis Testing
  • Visualization in Analytics
    • Visualization in R and Python to Support EDA
    • Regression Visualization
      • Scatter plots
      • Box plots
      • Density plots
      • Heatmaps
    • Classification Visualization
      • Bar plots
      • Parallel coordinates plot
      • Violin plots
      • Contour plots
  • Summary
• 5. Exploratory Data Analysis with R and Python
  • Data Quality
    • Data Quality Characteristics
    • Data Profiling
  • Clustering and Unsupervised Learning
    • Purpose of Unsupervised Learning
    • Example of Clustering Impacting Supervised Learning
    • K-Means Clustering
    • Hierarchical Clustering
    • Other Unsupervised Methods Used in EDA
  • Identifying Outliers
    • Outliers in Regression
    • Outliers in Classification
  • Data Preparation for Modeling
    • Sampling
      • Random sampling
      • Stratified sampling
      • Systematic sampling
      • Cluster sampling
      • Bootstrap sampling
      • Oversampling and undersampling
      • Importance of sampling in model building
    • Training and Testing
    • Data Transformation
      • Data formatting
        • One-hot encoding
        • Binning
      • Derived attributes
      • Scaling, normalization, and standardization
    • Data Manipulation
  • Selecting and Reducing Features
    • Feature Selection
      • Filter methods
      • Iterative methods
      • Wrapper methods
      • Embedded methods
    • Feature Reduction Techniques
      • Feature reduction for regression
        • PCA
        • LDA
      • Feature reduction for classification
  • Summary
• 6. Application and Evaluation of Modeling in R and Python
  • Modeling Steps
    • Model Selection and Training
    • Model Evaluation
    • Model Optimization
    • Model Deployment
    • Model Monitoring and Maintenance
  • Selecting the Right Algorithm
  • Regression
    • Common Use Cases
    • Linear Regression Equation
    • Linear Regression in R
    • Linear Regression in Python
      • Coefficients and statistics
      • Residual and model diagnostics
    • Linear Regression Use Case
    • Other Types of Regression
      • Polynomial regression
      • Multivariate regression
      • Time series regression
      • LASSO regression
      • Ridge regression
      • Elastic net
    • Challenges with Regression Models
    • Other Algorithms for Regression
    • Decision Trees for Regression
      • Distinguishing regression trees from classification trees
    • Linear Regression Evaluation
      • Model evaluation in R
      • Model evaluation in Python
  • Classification
    • Common Use Cases
    • Classification Algorithms
    • Classification in R
    • Classification in Python
    • Classification Use Case: Telecom Churn
      • Python example
    • Classification Evaluation
      • Metrics
      • Confusion matrix
      • Model evaluation in R
      • Model evaluation in Python
      • Calculating classification metrics in Python
    • Classification Use Case Evaluation
  • Summary
• 7. Modeling and Algorithm Choice
  • Algorithms
  • Algorithm Criteria
    • Problem Type
      • Classification problems
      • Regression problems
      • Clustering problems
      • Dimensionality reduction
    • Interpretable Models
      • Linear models
      • Decision trees
      • Ensemble models
      • Generalized additive models
    • Prediction Accuracy
      • Complexity and capacity
      • Ensemble methods
      • SVMs
      • Feature engineering
    • Training Speed
      • Algorithmic efficiency
      • Data size and quality
      • Parallelization and distributed computing
      • Algorithm selection based on problem complexity
      • Early stopping
    • Prediction Speed
      • Model complexity
      • Dimensionality reduction
      • Model training and serving architecture
      • Algorithm computational speed
      • Model pruning and quantization
    • Hyperparameter Tuning
      • Algorithm complexity and hyperparameter space
      • Automated hyperparameter tuning tools
      • Computational resources
      • Tuning versus performance trade-off
      • Sensitivity analysis
      • Cross-validation strategy
      • Example of hyperparameter tuning
    • Working with a Small Dataset
    • Working with a Large Dataset
    • Feature Interaction
      • Decision trees
      • Deep learning models
      • Kernel methods
      • Regularization techniques
    • Data Characteristics
      • Dimensionality
      • Feature type
      • Class distribution (balanced versus imbalanced)
      • Data quality and missing values
      • Underlying data distribution
  • Example: Selecting the Right Algorithm
    • Choosing the Right Algorithm to Predict Sales
      • Step 1: Understanding the problem type
      • Step 2: Model interpretation
      • Step 3: Model accuracy
      • Step 4: Training speed
      • Step 5: Prediction speed
      • Step 6: Parameter tuning
      • Step 7: Size of dataset
      • Step 8: Feature interaction
    • Evaluating the Criteria
    • Decision and Implementation
  • Summary
• 8. Model Operations
  • Overview of Model Operations
  • Model Operations Processes
  • Model Scoring
    • Model Scoring in R: Using Shiny Apps for Real-Time Scoring
    • Model Scoring in Python: Deploying Models with Streamlit
  • Model Monitoring
    • Key Metrics and Indicators for Model Performance Monitoring
    • Techniques for Automated Model Monitoring
      • Implementation in R: Building dashboards with shinydashboard
      • Implementation in Python: Utilizing visualization libraries like Matplotlib and Seaborn
        • Step 1: Install necessary packages
        • Step 2: Sample Python code
  • Model Retraining
    • Triggering Events for Model Retraining
    • Techniques for Automated Model Retraining
    • Implementation in R: Using cron Jobs for Scheduled Retraining
    • Implementation in Python: Leveraging Tools Like Airflow for Workflow Management
  • Generating Reports
    • Content and Structure of Final Reports
    • Techniques for Automated Report Generation
    • Implementation in R: Generating Reports with R Markdown and knitr
      • Step 1: Document setup
      • Step 2: Code integration
      • Step 3: Knitting process
      • Step 4: Automation
    • Implementation in Python: Creating Reports with Jupyter Notebooks and nbconvert
      • Step 1: Notebook development
      • Step 2: Markdown cells
      • Step 3: nbconvert conversion
      • Step 4: Automation
  • Version Control and Model Reproducibility
  • Collaboration and Documentation Practices
  • ModelOps Use Cases
    • Retail Sales Forecasting: Automation of Scoring and Monitoring
    • Fraud Detection: Dynamic Model Retraining and Reporting
    • Customer Churn Prediction: Scheduled Model Retraining and Final Report Generation
  • Integration with Existing Systems and Infrastructure
  • Future Direction of MLOps
  • Summary
• 9. Advanced Visualization
  • Advanced Visualization with R Shiny
    • What Is R Shiny?
    • Key Features and Capabilities of R Shiny
      • Interactive web applications
      • Reactivity
      • Accessibility
      • Extensibility
      • Community and support
    • Setting Up Your Environment
    • Building Your First Shiny App
      • Structure of a Shiny app: UI and server components
      • A simple example app explained
      • Interactive graphics in Shiny
      • Using Plotly and ggplot2 for dynamic plots
      • ggplot2
      • Plotly
    • Advanced UI Development
      • Customizing appearance with HTML and CSS
      • HTML (HyperText markup language)
      • CSS
      • Relationship and usage
      • Using shinydashboard for creating dashboards
      • Shiny widgets and extensions
    • Example: Creating a Dashboard to Monitor Real-Time Sales
  • Learning Python Visualization
    • Overview of Visualization in Python
    • Common Libraries: Matplotlib, Seaborn, Plotly, and Dash
    • Matplotlib: Foundations of Visualization in Python
    • Customizing Plots with Styles and Colors
    • Statistical Plots: Scatter Plots, Heatmaps, Violin Plots
    • Interactive Plots with Plotly
    • 3D Plotting with Matplotlib and Plotly
    • Geospatial Data Visualization
    • Dashboard Creation: Use Plotly Dash
    • Case Study: Using Python for an Advanced Visualization Project
  • Choosing Between R Shiny and Python Visualization
  • Summary
• 10. Working with Modern Data Types in Analytics
  • Semistructured Data (JSON)
    • Using Python for JSON Data
      • Loading and parsing JSON data
      • Extracting data from nested JSON structures
      • Transforming JSON data into Pandas DataFrames
      • Cleaning and normalizing JSON data
      • Analyzing JSON data with Python
    • Using R for JSON Data
      • Loading and parsing JSON data
      • Extracting data from nested JSON structures
      • Transforming JSON data into data frames with R
      • Cleaning and normalizing JSON data
      • Analyzing JSON data with R
  • Social Media Data
    • Types of Social Media Data
      • Posts
      • Comments
      • Likes and reactions
      • Shares and reposts
      • Direct messages
    • Using Python for Social Media Data Analysis
      • X data extraction using Tweepy
        • Setting up the X API
        • Fetching posts using Tweepy
        • Cleaning and processing X posts with Tweepy
      • Facebook and data extraction using Facebook Graph API
        • Setting up Facebook Graph API
        • Data extraction using Facebook Graph API
      • Sentiment analysis on social media data in Python
        • Performing sentiment analysis with TextBlob or NLTK libraries
        • Visualizing sentiment trends with Matplotlib or Seaborn libraries
    • Using R for Social Media Data Analysis
      • X data extraction using rtweet
        • Fetching posts using rtweet
        • Cleaning and processing tweet data using rtweet
        • Analyzing and visualizing post data
      • Facebook and Instagram data extraction
        • Installing necessary packages
        • Fetching Facebook data
        • Fetching Instagram data
        • Cleaning and processing data
        • Sentiment analysis on social media data in R
  • Image Data
    • Image Processing in Python
    • Image Processing in R
  • Video Data
    • Using Python for Video Data
      • Extracting frames and saving them as images
      • Analyzing video content
        • Basic image processing
        • Object detection using pretrained models
      • Storing and visualizing video data insights
        • Creating visual summaries of video analysis
    • Using R for Video Data
      • Extracting frames from video
      • Extracting frames and saving them as images
      • Analyzing video content
      • Storing and visualizing video data insights
      • Creating visual summaries of video analysis
  • Summary
• 11. Measuring Business Value from Analytics and the Role of AI
  • What Is Business Value in Analytics?
    • Strategic Impact
    • Operational Efficiency
    • Customer Satisfaction and Loyalty
  • Metrics and KPIs for Measuring Business Value
    • Financial Metrics
    • Operational Metrics
    • Customer Metrics
    • Aligning Metrics with Organizational Goals and Objectives
    • Leveraging Metrics to Demonstrate Value
    • Metrics and KPIs in Practice
  • Business Case Examples of Value for Analytics
    • Step 1: Problem Definition and Setting Measurable Outcomes
    • Step 2: Identifying Metrics to Measure Success and Failure
    • Step 3: Implementing Analytics Solutions
    • Step 4: Measuring and Demonstrating Value
    • Step 5: Reporting and Continuous Improvement
  • AI and Generative AI in Business Analytics
    • Introduction to Generative AI
    • Applications in Product Design
    • Applications in Content Creation
    • Applications in Marketing
    • Enhancing Customer Experience
    • Improving Operational Efficiency
    • Future Prospects and Challenges
  • Use Cases for AI and Generative AI in Business Analytics
    • Use Case 1: AI-Driven Customer Insights and Recommendations
    • Use Case 2: Generative AI in Content Creation
    • Use Case 3: AI-Powered Supply Chain Optimization
    • Use Case 4: Enhancing Decision Making with AI
    • Use Case 5: AI in Healthcare Analytics
    • Use Case 6: Generative AI for Personalized Customer Experiences
    • Use Case 7: AI in Retail Analytics
  • Addressing Factual Inconsistencies and Human-AI Collaboration
    • Future Prospects
  • Challenges and Considerations
    • Integration Challenges and Scalability in Deploying AI Solutions
    • Mitigating Biases and Ensuring Fairness in AI-Driven Decisions
    • Technical and Organizational Challenges in AI Deployment
    • Cost and Resource Considerations
    • Future-Proofing AI Investments
  • Summary
• Index

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