Preface 1． Introduction Telling Better Stories with Data A Quick Look： Data Science for SAP Professionalr>A Quick Look： SAP Basics for Data Scientistr>Getting Data Out of SAP Roles and Responilitier>Summary 2． Data Science for SAP Professionalr>Machine Learning Supervised Machine Learning Unsupervised Machine Learning Semi-Supervised Machine Learning Reinforcement Macl'rine Learning Neural Networkr>Summary 3． SAP for Data Scientistr>Getting Started with SAP The ABAP Data Dictionary Tabler>Structurer>Data Elements and Domainr>Where-Used ABAP QuickViewer SE16 Export OData Servicer>Core Data Servicer>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 Indicatorr>Removing Extraneous Columnr>Whitespace Numberr>Phase 3： Analyzing Our Data DataExplorer Discrete Featurer>Continuous Featurer>Phase 4： Modeling Our Data TensorFlow and Kerar>Training and Testing Split Shaping and One-Hot Encoding Reciper>Preparing Data for the Neural Network Resultr>Summary 5． Anomaly Detection with R and Python Types of Anomalier>Tools in R AnomalyDetection Anomalize Getting the Data SAP ECC System SAP NetWeaver Gateway SQL Server Finding Anomalier>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-Meanr>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 Clusterr>k-Means Clustering k-Medoid Clustering Hierarchical Clustering Manual RFM Step 4： Report the Findingr>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 Analysir>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 Professionalr>Chapter 3： SAP for Data Scientistr>Chapter 4： Exploratory Data Analysir>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 Recommendationr>Be Creative Be Practical Enjoy the Ride Stay in Touch Index
