Practical data science with SAP machine learning techniques for enterprise data
| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media
2019
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| Edition: | First edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Chapter 8. Association Rule MiningUnderstanding Association Rule Mining; Support; Confidence; Lift; Apriori Algorithm; Operationalization Overview; Collecting the Data; Cleaning the Data; Analyzing the Data; Fiori; Summary; Chapter 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; Chapter 10. Conclusion; Original Mission; Recap; Chapter 1: Introduction; Chapter 2: Data Science for SAP Professionals
- Includes bibliographical references and index
- Neural NetworksSummary; Chapter 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; Chapter 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 KerasTraining and Testing Split; Shaping and One-Hot Encoding; Recipes; Preparing Data for the Neural Network; Results; Summary; Chapter 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; Finding Anomalies; PowerBI and R; PowerBI and Python; Summary; Chapter 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; Step 5: Evaluate Model; Predicting Sales in Python
- Step 1: Identify DataStep 2: Gather Data; Step 3: Explore Data; Step 4: Model Data; Step 5: Evaluate Model; Summary; Chapter 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
- Intro; Copyright; Table of Contents; Preface; How to Read This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Online Learning; How to Contact Us; Acknowledgments; Chapter 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; Roles and Responsibilities; Summary; Chapter 2. Data Science for SAP Professionals; Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Reinforcement Machine Learning