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Practical data science with SAP : machine learning techniques for enterprise data

Author: Greg Foss; Paul Modderman
Publisher: Sebastopol, CA : O'Reilly Media, [2019] ©2019
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:

Are you using SAP ERP and eager to unlock the enormous value of its data? With this practical guide, SAP veterans Greg Foss and Paul Modderman show you how to use several data analysis tools to solve  Read more...

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Foss, Greg.
Practical Data Science with SAP : Machine Learning Techniques for Enterprise Data.
Sebastopol : O'Reilly Media, Incorporated, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Greg Foss; Paul Modderman
ISBN: 9781492046417 1492046418 9781492046455 1492046450
OCLC Number: 1121200581
Description: 1 online resource (1 volume) : illustrations
Contents: 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 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 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
Responsibility: Greg Foss and Paul Modderman.

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