سرفصل ها و محتوای دوره آموزش Business Intelligence using SSAS 2022 

(Data Warehousing , OLAP, SSIS & ETL, Data Mining, Presentation)

طول دوره : 72 ساعت

پیش نیاز دوره: Query نویسی. 

اهداف دوره :

آشنایی با طراحی و پیاده سازی Data Warehouse بصورت Subject Oriented.
آشنایی با ابزار SSIS درطراحی و پیاده سازی فرآیند ETL (Extract/ Transform / Load) جهت جمع آوری، یکسان سازی، تجمیع و بارگذاری داده ها در انباره داده.
آشنایی با ابزار SSAS جهت طراحی و پیاده سازی ساختارهای چند بعدی (Multi Dimensional) جهت آنالیز داده ها و چگونگی نگارش گزارش های تحلیلی با استفاده از زبان MDX. 
طراحی مدلهای Tabular در محیط SSAS و Power Pivot و استفاده از زبان DAX برای نوشتن گزاره های تحلیلی در این مدل.
آشنایی با ابزار Power BI ,SSRSو Excel, Power View for Excel جهت ساخت گزارشات داشبوردی.
آشنایی مقدماتی با چگونگی ایجاد ساختارهاو مدل های داده کاوی جهت انجام تحلیل های پیشرفته تر.
راه اندازی سیستم های BI وساخت داشبوردهای مدیریتی.

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سرفصل دوره :

Introduction to BI (2 Hours)

Data Warehouse Design (5 Hours)

  Understanding BI

  Understanding Data Warehouse Design

  Stages of Making a BI System

  Designing Data Warehouse

OLAP Modeling

    Star Schema

    Snowflake Schema

    Constellation Schema

Designing Dimension

Designing Fact

Extract,Transform & Load Data (22 Hours)

Introduction to SSIS

Getting Started

Creating SSIS Packages and Data Sources

Creating and Editing Control Flow Objects

Using the Maintenance Plan Tasks

Using Containers

Sequence Container

For Loop Container

Foreach Loop Container

Using Expressions & Variables

Using Parameters

Loading a Data Warehouse

Data Extraction

Data Transformation

Changing Data Types with the Data Conversion Transform

Creating Columns with the Derived Column Transform

Rolling Up Data with the Aggregate Transform

Ordering Data with the Sort Transform

Joining Data using Lookup/Merge Join

Combining Multiple Inputs with the Union All

Auditing Data with the Row Count Transform

Separating Data with the Conditional Split Transform

Altering Rows with the OLE DB Command Transform

Using Cache Transform Component

Dimension Table Loading

Using SCD(Slowly Changing Dimensions)

Fact Table Loading

Using CDC(Change Data Capture)

SSAS Processing

Implementing Multi-Dimensional Model (20 Hours)

Designing DSV(Data Source View)

Dimensions: Attributes & Members

Dimensions: Hierarchies

Measure Group & Measures

Demonstrate Using Excel Pivot Table

Advanced Dimension Designing

Introduction to MDX language (Multi-Dimensional Expressions)

MDX Query

MDX Expressions

Using Calculations

Calculated Member

Named Set

Script Command

Implementing KPI

Adding Translation

Using Perspectives

Managing Data Warehouse

Elementary Partitioning

Elementary Processing

Deployment

 Managing Security

Implementing Tabular Model (8 Hours)

Tabular Model Concepts

  Tabular Model Concepts

Comparison of Multidimensional and Tabular Models

Tabular Model Implementation

  Fetching the Data

  Designing Data Model

Active vs Inactive Relationship

Bidirectional Relationship

   Introduction to DAX language (Data Analysis Expressions)

Calculated Attributes

Calculated Tables

Measures

   Demonstrate Using Excel Pivot Table

   Implementing KPI

   Implementing Hierarchy

   Using Perspectives

   Managing Data Warehouse

Elementary Partitioning

Elementary Processing

Deployment

  Managing Security

Self Service BI (Power Pivot)

Self Service BI Concept

Power Pivot Implementation Using Excel

Reporting and Dashboards Design Using PowerBI (8 Hours)

   Visualization best practices and Choosing the right visuals
    Overview variations, fields and functionality of primary Visuals
      Visuals interactions, Slicers, Tooltips and Drilling Through
     Report/Page/Visual filtering
     Mobile-Optimized Reports
     Applying custom visuals

Formatting

Introduction to Data Mining (7 Hours)

Understanding Data Mining

Data Mining Concepts

The Data Mining Process

Understanding Key Concepts

Attribute

State/Value

Case/Nested Case / Case Table / Nested Table

Keys ( Case Key/ Nested Key)

Inputs and Outputs

Implementing Mining Structure

Implementing Case Table

Implementing Nested Table

Partitioning Sets

Implementation Mining Model

Introduction to Data Mining Algorithms

Browsing and Querying Mining Models

Using Mining Model Viewer

Elementary Prediction with Mining Model Predictions

Introduction to MDX (Data Mining Extensions) in DQL mode