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  • 音像SAP实用数据科学()(美)格雷格·福斯,(加)保罗·莫德曼
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    • 作者: (美)格雷格·福斯,(加)保罗·莫德曼著 | (美)格雷格·福斯,(加)保罗·莫德曼编 | (美)格雷格·福斯,(加)保罗·莫德曼译 | (美)格雷格·福斯,(加)保罗·莫德曼绘
    • 出版社: 东南大学出版社
    • 出版时间:2020-06-01
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    • 作者: (美)格雷格·福斯,(加)保罗·莫德曼著| (美)格雷格·福斯,(加)保罗·莫德曼编| (美)格雷格·福斯,(加)保罗·莫德曼译| (美)格雷格·福斯,(加)保罗·莫德曼绘
    • 出版社:东南大学出版社
    • 出版时间:2020-06-01
    • 版次:1
    • 印次:1
    • 字数:406000
    • 页数:316
    • 开本:16开
    • ISBN:9787564188818
    • 版权提供:东南大学出版社
    • 作者:(美)格雷格·福斯,(加)保罗·莫德曼
    • 著:(美)格雷格·福斯,(加)保罗·莫德曼
    • 装帧:平装
    • 印次:1
    • 定价:99.00
    • ISBN:9787564188818
    • 出版社:东南大学出版社
    • 开本:16开
    • 印刷时间:暂无
    • 语种:暂无
    • 出版时间:2020-06-01
    • 页数:316
    • 外部编号:1202080826
    • 版次:1
    • 成品尺寸:暂无

    Preface

    1. Introduction

    Telling Better Stories with Data

    A ick Look: Data Science for SAP Professionals

    A ick 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 Maclrine Learning

    Neural Networks

    Summary

    3. SAP for Data Scientists

    Getting Started with SAP

    The ABAP Data Dictionary

    Tables

    Structures

    Data ElemensndDmains

    Where-Used

    ABAP ickViewer

    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

    SL 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

    Oraioalization 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

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