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SAP Courses

SAP Hana Training

The Oracle Database Training provides a thorough understanding of Oracle’s powerful database management system. This course covers everything from fundamental database concepts to advanced administrative tasks. Participants will learn about Oracle’s architecture, SQL querying, PL/SQL programming, and performance tuning.
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SAP HANA Training Course Syllabus

  • Overview of SAP HANA: Evolution and importance in modern data management.
  • Career prospects and opportunities for SAP HANA professionals.

Course content

  • Overview of Artificial Intelligence and Machine Learning
  • History and Evolution of AI and ML
  • Key Applications and Use Cases in Industry
  • Introduction to Python for AI and ML
  • SAP HANA Architecture Overview:

    • Row store vs. Column store.
    • Persistence layer.
    • In-memory computing engine.
  • SAP HANA Deployment Options:

    • On-premise vs. Cloud deployment.
  • Introduction to SAP HANA Studio.

  • Schema concepts in SAP HANA.

  • Creating and managing tables.

  • SAP HANA Views:

    • Attribute views.
    • Analytic views.
    • Calculation views.
  • Data provisioning techniques:

    • SAP Landscape Transformation (SLT).
    • SAP HANA Direct Extractor Connection (DXC).
    • SAP HANA Smart Data Integration (SDI).
  • Real-time data replication and integration.

  • System landscape and architecture.

  • Installation and setup of SAP HANA.

  • User and role management.

  • Monitoring and Performance Tuning:

    • SAP HANA Cockpit.
    • Performance optimization techniques.
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  • SAP HANA Advanced Analytics:

    • Predictive analytics using SAP HANA.
    • Text analytics and search.
  • SAP HANA Security:

    • Authentication and authorization.
    • Role-based security.
  • SAP HANA Cloud:

    • Overview of SAP HANA Cloud Platform.
    • Integration with other SAP and non-SAP systems.
  • Overview of In-Memory Computing.
  • Evolution of SAP HANA.
  • Key features and capabilities of SAP HANA.
  • Data Collection and Cleaning
  • Handling Missing Values and Outliers
  • Data Normalization and Standardization
  • Feature Selection and Extraction Techniques
  • Linear Regression and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Model Evaluation and Cross-Validation
  • Clustering Techniques (K-Means, Hierarchical Clustering)
  • Dimensionality Reduction (PCA, t-SNE)
  • Anomaly Detection and Association Rules
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  • Natural Language Processing (NLP) and Text Analysis
  • Reinforcement Learning and Agent-Based Models
  • Transfer Learning and Pre-trained Models
  • AI Ethics and Bias Mitigation
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  • Real-world Project Implementation
  • Case Study Analysis and Solution Development
  • Model Deployment and Monitoring
  • Presentation and Documentation of Project
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Instructors
Joshua Hamilton

Web Developer

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