Data Modeling Online Training

Introduction

Data modeling stands as a foundation plan for users to communicate information requirements about various business processes as well as for developers to consider those requirements while constructing an effective physical database. Data Modeling is today’s most essential data management tool for organizations to setup and configure most complex data from various sources into an organized information flow that enables key personnel take business informed decisions. The Data Modeling Training at IQ Technologies stands at par with industry’s real time scenarios.

Why to Learn Data Modeling?

In the modern days businesses are dealing with large unstructured data to improve their business by predicting the sales, marketing in better ways, deriving new strategies all because of data available but the data is too huge and cannot be easily understood. So we designed this Data Modeling course in such a way that you can easily draw data flow representations from complex system designs to simple and efficient data flows and processes. You can re-design and create a blueprint for the design. Data Modeling Training gives you complete knowledge of designing the flowchart that represents the relation between data-oriented structures and identifies entity types.

COURSE SUMMARY

Course Name Data Modeling
Course Contents Fundamentals of Data Modeling
Course Duration 30 Hours with Flexible timings
Course Delivery Instructor Led-Live Online Training
Course Eligibility Any Graduate
Ideal For Fresher, aspiring  job seekers in the field of BO
Next Batch Please visit the schedule section

Course Objectives

HIGHLIGHTS OF DATA MODELING

  • Practical guide to create Data Model for Business Intelligence and Data Warehouse applications
  • Detailed Coverage of Data warehouse , Database and Business Intelligence conception
  • SQL framework in relation with Data Modeling
  • Knowledge on customizing pre built data models.
  • Course curriculum designed from job standpoint.

CORE BENEFITS OF LEARNING DATA MODELING

The growing needs of global data management are challenging organizations to design well-organized data models in place or preen the existing data models to support the dynamic business requirements, thus creating wide opportunities for organizations as well as professionals for innovating critical business objects. Data Modeling is therefore a sought after specialization.

KEY FOCUS AREA

Data Modeling Key Concepts, Layer Architecture, Relational Data Models, Schemas, Methods and Attributes, Design data Model with definite user group information requirements.

Course Curriculum

Data Modeling is vital to design data life cycle of organization’s business portfolios. An efficient data model organizes business relevant objects, their relationships, integrates with other applications to provide accurate, reliable and secure information to key users. This course is designed to help aspirants understand the essence of data modeling while building effective business models.

CONTENTS :

NEED FOR DATA MODELING

  • Introducing the Data Modeling concept with real time scope and benefits
  • Role and Responsibilities of industry wide Data Modeling Experts
  • Business requirements analysis in data modeling
  • Types of Data Models and Databases
  • Data Model Components , quality ,characteristics , Trends
  • Overview of Development Life Cycle

DATA MODELING NITTY-GRITTY

  • Data Models at various levels
  • Conceptual model creation
  • Attributes Vs Entities
  • Identifiers
  • Relationships
  • Specialization and generalization of Entity
  • Model Diagrams
  • Logical Data Model design : Step by step Transition
  • Physical Data Model : Step by Step Transition

ANATOMY

  • Definition of Entity , Types
  • Entity Object Sets , Categorization
  • Sub types Vs Super types
  • Conceptual Vs Physical
  • Entity Validation
  • Recursive Structures
  • Complete set of correct entities
  • Modeling time data

ATTRIBUTE AND IDENTIFIER

  • Definition, features ,value set, types , domain
  • Defining attribute in data model
  • Simple Vs Composite Attribute
  • Concept of Stored and derived values
  • Purpose of Identifier
  • Define and Set key for Identifier

RELATIONSHIPS

  • Definition, Types , Degree of dependency
  • Constraints : Structural , cardinal & Participation
  • Relationship Attributes
  • Identifying Relationship
  • Optional Conditions , Special Cases , Design Issues
  • Aggregation , Gerund , Access Pathway
  • Relationship Models with Multiple Concept: One – One , One –Many
  • Structures in Relationship
  • Concept of Redundancy in Relationships
  • Relationship Checklist for completeness and correctness

DATA MODELING – NORMALIZATION

  • Map Relations from requirements
  • Figure out possible errors and resolution
  • Normalization Definition , Purpose and Methodology
  • Detailed Coverage of Normalization Steps
  • Normal Forms and evolution
  • BCNF
  • Domain Key Normal Form
  • Normalization synopsis

DESIGN DATA WAREHOUSE WITH DATA MODELING

  • Data Warehouse fundamentals
  • Information Grouping : Strategic Vs Operational
  • Decision Support Systems and types
  • Dimensional Modeling , Analysis
  • Schema : Star , Snow flake
  • OLAP Conceptual , Feature and Functionality
  • Logical Overview of Data Mining , Data Preprocessing and Data Modeling
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