Preface
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

2�� Data Science for SAP Professionals
Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Reinforcement Macl'rine Learning
Neural Networks
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
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
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