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Spis treści

Practical Data Science with SAP. Machine Learning Techniques for Enterprise Data eBook -- spis treści

• Preface
  • How to Read This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction
  • Telling Better Stories with Data
  • A Quick Look: Data Science for SAP Professionals
  • A Quick Look: SAP Basics for Data Scientists
    • Getting Data Out of SAP
      • BAPIs: Using the NetWeaver RFC Library
      • OData
      • Other ways to get data
        • Web services
        • Direct database access
        • Screen dumps to Excel
  • Roles and Responsibilities
  • Summary
• 2. Data Science for SAP Professionals
  • Machine Learning
    • Supervised Machine Learning
      • Linear regression
      • Logistic regression
      • Decision trees
      • Random forest
    • Unsupervised Machine Learning
      • k-means clustering
      • Naive Bayes
      • Hierarchical clustering
    • Semi-Supervised Machine Learning
    • Reinforcement Machine Learning
      • Hidden Markov models
      • Q-learning
  • Neural Networks
    • Feed-forward propagation
    • Backward propagation
    • Gradient descent
    • Learning rate
    • Neuron
    • Functions
      • Single layer perceptron
      • Multilayer perceptron
      • Convolutional network
      • Recursive neural network
      • Temporal networks
      • Autoencoder
      • Generative adversarial network
  • Summary
• 3. SAP for Data Scientists
  • Getting Started with SAP
  • The ABAP Data Dictionary
    • Tables
    • Structures
    • Data Elements and Domains
    • Where-Used
    • ABAP QuickViewer
    • SE16 Export
  • OData Services
  • Core Data Services
  • Summary
• 4. Exploratory Data Analysis with R
  • The Four Phases of EDA
  • Phase 1: Collecting Our Data
    • Importing with R
  • Phase 2: Cleaning Our Data
    • Null Removal
    • Binary Indicators
  • Removing Extraneous Columns
    • Whitespace
    • Numbers
  • Phase 3: Analyzing Our Data
    • DataExplorer
    • Discrete Features
    • Continuous Features
  • Phase 4: Modeling Our Data
    • TensorFlow and Keras
    • Training and Testing Split
    • Shaping and One-Hot Encoding
    • Recipes
    • Preparing Data for the Neural Network
    • Results
  • Summary
• 5. Anomaly Detection with R and Python
  • Types of Anomalies
  • Tools in R
    • AnomalyDetection
    • Anomalize
    • Getting the Data
    • SAP ECC System
    • SAP NetWeaver Gateway
    • SQL Server
      • SQL Server Integration Services (SSIS)
  • Finding Anomalies
    • PowerBI and R
    • PowerBI and Python
  • Summary
• 6. Predictive Analytics in R and Python
  • Predicting Sales in R
    • Step 1: Identify Data
    • Step 2: Gather Data
    • Step 3: Explore Data
    • Step 4: Model Data
      • Plots for prediction
    • Step 5: Evaluate Model
  • Predicting Sales in Python
    • Step 1: Identify Data
    • Step 2: Gather Data
    • Step 3: Explore Data
    • Step 4: Model Data
    • Step 5: Evaluate Model
  • Summary
• 7. Clustering and Segmentation in R
  • Understanding Clustering and Segmentation
    • RFM
    • Pareto Principle
    • k-Means
    • k-Medoid
    • Hierarchical Clustering
    • Time-Series Clustering
  • Step 1: Collecting the Data
  • Step 2: Cleaning the Data
  • Step 3: Analyzing the Data
    • Revisiting the Pareto Principle
    • Finding Optimal Clusters
    • k-Means Clustering
    • k-Medoid Clustering
    • Hierarchical Clustering
    • Manual RFM
  • Step 4: Report the Findings
    • R Markdown Code
    • R Markdown Knit
  • Summary
• 8. Association Rule Mining
  • Understanding Association Rule Mining
    • Support
    • Confidence
    • Lift
    • Apriori Algorithm
  • Operationalization Overview
  • Collecting the Data
  • Cleaning the Data
  • Analyzing the Data
    • Fiori
  • Summary
• 9. Natural Language Processing with the Google Cloud Natural Language API
  • Understanding Natural Language Processing
    • Sentiment Analysis
    • Translation
  • Preparing the Cloud API
  • Collecting the Data
  • Analyzing the Data
  • Summary
• 10. Conclusion
  • Original Mission
  • Recap
    • Chapter 1: Introduction
    • Chapter 2: Data Science for SAP Professionals
    • Chapter 3: SAP for Data Scientists
    • Chapter 4: Exploratory Data Analysis
    • Chapter 5: Anomaly Detection with R and Python
    • Chapter 6: Prediction with R
    • Chapter 7: Clustering and Segmentation in R
    • Chapter 8: Association Rule Mining
    • Chapter 9: Natural Language Processing with the Google Cloud Natural Language API
  • Tips and Recommendations
    • Be Creative
    • Be Practical
    • Enjoy the Ride
  • Stay in Touch
• Index