Practical Data Science with SAP

Book Description

Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data.

Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths.

You'll explore:

  • Examples of how data analysis can help you solve several SAP challenges
  • Natural language processing for unlocking the secrets in text
  • Data science techniques for data clustering and segmentation
  • Methods for detecting anomalies in your SAP data
  • Data visualization techniques for making your data come to life

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Table of Contents

  1. Preface
    1. How to Read This Book
    2. Conventions Used in This Book
    3. Using Code Examples
    4. O’Reilly Online Learning
    5. How to Contact Us
    6. Acknowledgments
  2. 1. Introduction
    1. Telling Better Stories with Data
    2. A Quick Look: Data Science for SAP Professionals
    3. A Quick Look: SAP Basics for Data Scientists
      1. Getting Data Out of SAP
    4. Roles and Responsibilities
    5. Summary
  3. 2. Data Science for SAP Professionals
    1. Machine Learning
      1. Supervised Machine Learning
      2. Unsupervised Machine Learning
      3. Semi-Supervised Machine Learning
      4. Reinforcement Machine Learning
    2. Neural Networks
    3. Summary
  4. 3. SAP for Data Scientists
    1. Getting Started with SAP
    2. The ABAP Data Dictionary
      1. Tables
      2. Structures
      3. Data Elements and Domains
      4. Where-Used
      5. ABAP QuickViewer
      6. SE16 Export
    3. OData Services
    4. Core Data Services
    5. Summary
  5. 4. Exploratory Data Analysis with R
    1. The Four Phases of EDA
    2. Phase 1: Collecting Our Data
      1. Importing with R
    3. Phase 2: Cleaning Our Data
      1. Null Removal
      2. Binary Indicators
    4. Removing Extraneous Columns
      1. Whitespace
      2. Numbers
    5. Phase 3: Analyzing Our Data
      1. DataExplorer
      2. Discrete Features
      3. Continuous Features
    6. Phase 4: Modeling Our Data
      1. TensorFlow and Keras
      2. Training and Testing Split
      3. Shaping and One-Hot Encoding
      4. Recipes
      5. Preparing Data for the Neural Network
      6. Results
    7. Summary
  6. 5. Anomaly Detection with R and Python
    1. Types of Anomalies
    2. Tools in R
      1. AnomalyDetection
      2. Anomalize
      3. Getting the Data
      4. SAP ECC System
      5. SAP NetWeaver Gateway
      6. SQL Server
    3. Finding Anomalies
      1. PowerBI and R
      2. PowerBI and Python
    4. Summary
  7. 6. Predictive Analytics in R and Python
    1. Predicting Sales in R
      1. Step 1: Identify Data
      2. Step 2: Gather Data
      3. Step 3: Explore Data
      4. Step 4: Model Data
      5. Step 5: Evaluate Model
    2. Predicting Sales in Python
      1. Step 1: Identify Data
      2. Step 2: Gather Data
      3. Step 3: Explore Data
      4. Step 4: Model Data
      5. Step 5: Evaluate Model
    3. Summary
  8. 7. Clustering and Segmentation in R
    1. Understanding Clustering and Segmentation
      1. RFM
      2. Pareto Principle
      3. k-Means
      4. k-Medoid
      5. Hierarchical Clustering
      6. Time-Series Clustering
    2. Step 1: Collecting the Data
    3. Step 2: Cleaning the Data
    4. Step 3: Analyzing the Data
      1. Revisiting the Pareto Principle
      2. Finding Optimal Clusters
      3. k-Means Clustering
      4. k-Medoid Clustering
      5. Hierarchical Clustering
      6. Manual RFM
    5. Step 4: Report the Findings
      1. R Markdown Code
      2. R Markdown Knit
    6. Summary
  9. 8. Association Rule Mining
    1. Understanding Association Rule Mining
      1. Support
      2. Confidence
      3. Lift
      4. Apriori Algorithm
    2. Operationalization Overview
    3. Collecting the Data
    4. Cleaning the Data
    5. Analyzing the Data
      1. Fiori
    6. Summary
  10. 9. Natural Language Processing with the Google Cloud Natural Language API
    1. Understanding Natural Language Processing
      1. Sentiment Analysis
      2. Translation
    2. Preparing the Cloud API
    3. Collecting the Data
    4. Analyzing the Data
    5. Summary
  11. 10. Conclusion
    1. Original Mission
    2. Recap
      1. Chapter 1: Introduction
      2. Chapter 2: Data Science for SAP Professionals
      3. Chapter 3: SAP for Data Scientists
      4. Chapter 4: Exploratory Data Analysis
      5. Chapter 5: Anomaly Detection with R and Python
      6. Chapter 6: Prediction with R
      7. Chapter 7: Clustering and Segmentation in R
      8. Chapter 8: Association Rule Mining
      9. Chapter 9: Natural Language Processing with the Google Cloud Natural Language API
    3. Tips and Recommendations
      1. Be Creative
      2. Be Practical
      3. Enjoy the Ride
    4. Stay in Touch
  12. Index

Product Information

  • Title: Practical Data Science with SAP
  • Author(s): Greg Foss, Paul Modderman
  • Release date: September 2019
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492046448