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  • 正版 SAP实用数据科学 Greg 东南大学出版社 9787564188818 书籍
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    • 作者: Greg著 | Greg编 | Greg译 | Greg绘
    • 出版社: 东南大学出版社
    • 出版时间:2019-06-01
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    • 作者: Greg著| Greg编| Greg译| Greg绘
    • 出版社:东南大学出版社
    • 出版时间:2019-06-01
    • 版次:1
    • 印刷时间:2020-06-01
    • 字数:406000
    • 页数:316
    • 开本:24开
    • ISBN:9787564188818
    • 版权提供:东南大学出版社
    • 作者:Greg
    • 著:Greg
    • 装帧:平装
    • 印次:暂无
    • 定价:99.00
    • ISBN:9787564188818
    • 出版社:东南大学出版社
    • 开本:24开
    • 印刷时间:2020-06-01
    • 语种:英语
    • 出版时间:2019-06-01
    • 页数:316
    • 外部编号:9802271
    • 版次:1
    • 成品尺寸:暂无

    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 Maclrine 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

      你是否正在使用SAP ERP系统并迫切希望释放其数据的巨大价值?通过这本实用指导书,SAP专家Greg Foss和Paul Modderman为你展示如何使用若干数据分析工具来解决SAP数据中存在的有趣问题。你将跟随一个贯穿全书的虚构公司,学会处理真实场景中遇到的问题。
      使用真实数据创建示例代码和可视化图,SAP业务分析师将学会实用的分析方法,从而获得对业务数据的更深入了解。数据工程师和数据科学家将探索如何将SAP数据添加到他们的分析过程中。通过对SAP流程和数据科学工具的深入研究,你将找到揭露数据真相的强大方法。

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