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 interesting problems with your SAP data. Throughout the book, youll follow a fictional company as it tackles real scenarios.
Using actual data to create example code and visualizations, SAP business analysts will learn practical methods for gaining deeper insights into their businesss data. Data engineers and data scientists will explore ways to add SAP data to their analysis processes. Through grounded explanations of both SAP processes and data science tools, youll discover powerful methods for discovering data truths.
Use data to tell revealing stories about your customers Model purchase requisition data using exploratory data analysis Create an anomaly detection system for SAP sales orders Use R and Python to make predictions on sales data Cluster and segment your customers based on their buying habits Use association rule learning to discover customer buying patterns Apply NLP to uncover the most highly actionable customer complaints
| Preface |
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Telling Better Stories with Data |
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A Quick Look: Data Science for SAP Professionals |
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A Quick Look: SAP Basics for Data Scientists |
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8 | (3) |
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Roles and Responsibilities |
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11 | (1) |
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2 Data Science for SAP Professionals |
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Supervised Machine Learning |
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Unsupervised Machine Learning |
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18 | (3) |
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Semi-Supervised Machine Learning |
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Reinforcement Machine Learning |
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43 | (2) |
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3 SAP for Data Scientists |
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46 | (3) |
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49 | (19) |
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50 | (3) |
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53 | (1) |
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Data Elements and Domains |
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54 | (4) |
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58 | (4) |
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62 | (6) |
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68 | (1) |
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68 | (12) |
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80 | (11) |
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91 | (2) |
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4 Exploratory Data Analysis with R |
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93 | (40) |
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Phase 1 Collecting Our Data |
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96 | (11) |
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104 | (3) |
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Phase 2 Cleaning Our Data |
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107 | (1) |
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Removing Extraneous Columns |
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108 | (1) |
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108 | (1) |
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109 | (1) |
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Phase 3 Analyzing Our Data |
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109 | (12) |
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110 | (3) |
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113 | (4) |
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117 | (4) |
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Phase 4 Modeling Our Data |
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121 | (11) |
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122 | (1) |
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Training and Testing Split |
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122 | (1) |
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Shaping and One-Hot Encoding |
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123 | (1) |
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124 | (2) |
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Preparing Data for the Neural Network |
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126 | (4) |
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130 | (2) |
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132 | (1) |
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5 Anomaly Detection with R and Python |
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133 | (58) |
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134 | (1) |
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135 | (39) |
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135 | (1) |
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136 | (1) |
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136 | (1) |
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137 | (5) |
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142 | (11) |
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153 | (21) |
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174 | (15) |
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174 | (10) |
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184 | (5) |
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189 | (2) |
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6 Predictive Analytics in R and Python |
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191 | (34) |
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193 | (17) |
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193 | (1) |
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193 | (1) |
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194 | (1) |
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195 | (11) |
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206 | (4) |
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Predicting Sales in Python |
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210 | (12) |
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210 | (1) |
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210 | (6) |
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216 | (3) |
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219 | (1) |
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220 | (2) |
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222 | (3) |
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7 Clustering and Segmentation in R |
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225 | (42) |
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Understanding Clustering and Segmentation |
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226 | (7) |
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227 | (1) |
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228 | (1) |
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229 | (1) |
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230 | (1) |
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231 | (2) |
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233 | (1) |
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Step 1 Collecting the Data |
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233 | (1) |
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234 | (6) |
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Step 3 Analyzing the Data |
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240 | (18) |
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Revisiting the Pareto Principle |
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240 | (1) |
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241 | (3) |
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244 | (5) |
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249 | (4) |
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253 | (2) |
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255 | (3) |
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Step 4 Report the Findings |
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258 | (6) |
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261 | (1) |
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262 | (2) |
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264 | (3) |
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8 Association Rule Mining |
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267 | (22) |
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Understanding Association Rule Mining |
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269 | (1) |
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269 | (1) |
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269 | (1) |
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270 | (1) |
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270 | (1) |
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Operationalization Overview |
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270 | (1) |
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271 | (5) |
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276 | (1) |
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277 | (10) |
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282 | (5) |
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287 | (2) |
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9 Natural Language Processing with the Google Cloud Natural Language API |
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289 | (16) |
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Understanding Natural Language Processing |
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290 | (2) |
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290 | (2) |
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292 | (1) |
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292 | (6) |
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298 | (3) |
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301 | (2) |
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303 | (2) |
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305 | (6) |
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305 | (1) |
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306 | (2) |
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306 | (1) |
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Chapter 2 Data Science for SAP Professionals |
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306 | (1) |
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Chapter 3 SAP for Data Scientists |
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306 | (1) |
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Chapter 4 Exploratory Data Analysis |
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307 | (1) |
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Chapter 5 Anomaly Detection with R and Python |
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307 | (1) |
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Chapter 6 Prediction with R |
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307 | (1) |
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Chapter 7 Clustering and Segmentation in R |
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307 | (1) |
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Chapter 8 Association Rule Mining |
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307 | (1) |
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Chapter 9 Natural Language Processing with the Google Cloud Natural Language API |
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308 | (1) |
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308 | (1) |
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308 | (1) |
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308 | (1) |
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309 | (1) |
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309 | (2) |
| Index |
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Greg Foss fuses battle-tested deep SAP knowledge with a passion for all things data science. His SAP career spans all areas of the technology stack - server, database, security, back and front end development, and functional expertise. As an enterprise architect, he's been the steady guiding hand for years of managing, supporting, and enhancing SAP. As the founder of Blue Diesel Data Science, he focuses years of R, Python, machine learning algorithms, and analytics expertise on finding unique stories to tell from enterprise SAP data. Through Blue Diesel, Greg regularly contributes unique knowledge and insight into the data science blogging community, and is the principal developer and architect of VisionaryRX, an innovative pharmaceutical data dashboarding product.
Paul Modderman loves creating things and sharing them. His tech career has spanned web applications with technologies like .NET, Java, Python, and React to SAP solutions in ABAP, OData and SAPUI5, to cloud technologies in Google Cloud Platform, Amazon Web Services, and Microsoft Azure. He was principal technical architect on Mindset's certified solutions CloudSimple and Analytics for BW. He's an SAP Developer Hero, honored in 2017. Paul is the author of two books: Mindset Perspectives: SAP Development Tips, Tricks, and Projects, and the SAP Press published SAPUI5 and SAP Fiori: The Psychology of UX Design.
