نتایج جست و جو :
متخصص هوش تجاری Business Intelligence(بر اساس سرفصل های گواهینامه MCSA)
2 دی 1399

-Introduction to Data Warehousing

  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution

-Planning Data Warehouse Infrastructure

  • Considerations for Data Warehouse Infrastructure
  • Planning Data Warehouse Hardware

-Designing and Implementing a Data Warehouse

  • Data Warehouse Design Overview
  • Designing Dimension Tables
  • Designing Fact Tables
  • Physical Design for a Data Warehouse

-Creating an ETL Solution with SSIS

  • Introduction to ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow

-Implementing Control Flow in an SSIS Package

  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Implementing Control Flow in an SSIS Package
  • Managing Consistency
  • Using Transactions and Checkpoints

-Debugging and Troubleshooting SSIS Packages

  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in an SSIS Package

-Implementing a Data Extraction Solution

  • Planning Data Extraction
  • Extracting Modified Data

-Loading Data into a Data Warehouse

  • Planning Data Loads
  • Using SSIS for Incremental Loads
  • Using Transact-SQL Loading Techniques

-Enforcing Data Quality

  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Cleansing Data
  • Using Data Quality Services to Match Data
  • Deduplicating Data

-Master Data Services

  • Introduction to Master Data Services
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub

-Extending SQL Server Integration Services

  • Using Scripts in SSIS
  • Using Custom Components in SSIS

-Deploying and Configuring SSIS Packages

  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution

-Consuming Data in a Data Warehouse

  • Introduction to Business Intelligence
  • Enterprise Business Intelligence
  • Self-Service BI and Big Data

-Design a multidimensional business intelligence (BI) semantic model

         – Create a multidimensional database by using Microsoft SQL

         – Server Analysis Services (SSAS)

                    – Design, develop, and create multidimensional databases

                   – Select a storage model

         – Design and implement dimensions in a cube

Select an appropriate dimension model, such as fact, parent-child,roleplaying, reference, data mining, many-to-many –                    

                        , and slowly changing dimension

                    – Implement a dimension type

                    – Define attribute relationships 

         – Implement measures and measure groups in a cube

                    – Design and implement measures, measure groups, granularity,calculated measures, and aggregate functions

                   – Define semi-additive behavior

Design a tabular BI semantic model-

           – Design and publish a tabular data model

                    – Design measures, relationships, hierarchies, partitions,perspectives, and calculated columns

                    – Relationships

                    – Create a Time Table

                    – Publish from Microsoft Visual Studio

                    – Import from Microsoft PowerPivot

                    – Select a deployment option, including Processing Option,Transactional Deployment, and Query Mode

         – Configure, manage, and secure a tabular model

                    – Configure tabular model storage and data refresh

                    – Configure refresh interval settings

                    – Configure user security and permissions

                    – Configure row-level security

         – Develop a tabular model to access data in near real time Use DirectQuery with Oracle, Teradata, Excel, and PivotTables Convert in-memory queries to DirectQuery

-Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX)

 Create basic MDX queries –

                   – Implement basic MDX structures and functions, including tuples, sets, and TopCount

 Implement custom MDX solutions –       

                   – Create custom MDX or logical solutions for pre-prepared case tasks or business rules

                   – Define a SCOPE statement

 Create formulas by using the DAX language –       

                   – Use the EVALUATE and CALCULATE functions

                   – Filter DAX queries

                   – Create calculated measures

                   – Perform analysis by using DAX   

Configure and maintain SQL Server Analysis Services (SSAS)-

        – Plan and deploy SSAS

                   – Configure memory limits

                   – Configure Non-Union Memory Access (NUMA)

                   – Configure disk layout

                   – Determine SSAS instance placement

        – Monitor and optimize performance

                   – Monitor performance and analyze query plans by using Extended Events and Profiler

                   – Identify bottlenecks in SSAS queries

                   – Monitor processing and query performance

                   – Resolve performance issues

                   – Configure usability limits

                   – Optimize and manage model design

        – Configure and manage processing

                   – Configure partition processing

                   – Configure dimension processing

                   – Use Process Default, Process Full, Process Clear, Process Data, Process Add, Process Update, Process Index, Process Structure, and Process Clear Structure processing methods Configure Parallel, Sequential, and Writeback processing settings

        – Create Key Performance Indicators (KPIs) and translations

                   – Create KPIs in multidimensional models and tabular models

                   – Configure KPI options, including Associated measure group, Value Expression, Goal Expression, Status, Status expression, Trend, Trend expression, and Weight

                   – Create and develop translations

-Getting started to Power BI

-Introduction to Power BI

-Introduction to the Query editor

-Working on our Data Model – Data & Relationship View

-Working in the Report View to Visualize our Results

-Power BI Service & Power BI Mobile – How to Continue

-Other Data Sources

-Introduction to Reporting Service(SSRS)



SQL Server
17 شهریور 1398

SQL Server Installation-

Service Account-

Authentications-

collation settings-

Server Role-

Database Role-

schema concept-

Entities-

Primary Key & Foreign Key-

Relations-

Cardinality-

Join Concepts-

Data Types-

Exact numerics-

Approximate numerics-

Decimal-

Date and Time-

Character Strings-

BLOB-

Referential Integrity laws-

Check Constraints-

DDL Statements to CREATE / ALTER / DROP tables-

Working with Scalar Functions-

Using Case Expressions-

ALL-

DISTINCT-

TOP-

Table-

View-

(Derived Table and Common Table Expressio (CTE-

Table Valued Functions-

Synonyms-

Grouping Sets-

Conditional Aggregate Functions-

Casting and conversion-

Working with Date and Time data types-

UNION-

INTERSECT-

EXCEPT-

Update from existing data-

WRITE extension to the UPDATE command-

Deleting related data-

Truncate Table-

Working with Variables-

IF Statement-

CASE Expressions-

WHILE Loop-

Auto Commit Transactions-

BEGIN TRAN and compound transactions-



تربیت متخصص علم داده Data Science
26 تیر 1397

با علم داده data science شروع به سفر علمی داده های خود کنید، در این دوره آموزشی یاد می گیرید چه چیز باعث تبدیل شدن شما  به یک دانشمند داده data scientist می شود. یاد بگیرید که از داده های مختلفی از تجسم، تحلیلی و تکنیک های آماری استفاده کنید.
کارشناسان علم داده می‌توانند مهارت‌هایشان را برای دست یابی به طیف وسیعی از نتایج نهایی به کار گیرند. تعدادی از این مهارت ها به شرح ذیل هستند:
  • توانایی استخراج و تفسیر منابع داده
  • مدیریت حجم زیاد اطلاعات با سخت‌افزار
  • محدودیت‌های نرم‌افزاری و پهنای باند
  • ادغام منابع داده با یک دیگر
  • تضمین پایداری مجموعه‌های داده
  • مصورسازی داده برای فهم آن
  • ساخت مدل‌های ریاضی با استفاده از داده، مانند مدلهای ریگرسیون و طبقه بندی
  • مقایسه آماری مدلهای ریاضی گوناگون و انتخاب مدل برتر، فی المثل توسط آزمون آ/ب
  • به اشتراک گذاری یافته‌ها و دیدگاه‌ها در حوزه داده با متخصصان دیگر یا مخاطب عام
مباحثی که در این دوره آموزشی، به فراگیران آموزش داده می شود طبق سرفصل های عنوان شده توسط مایکروسافت برای کارشناسان علم داده می باشد که به تفصیل در ذیل آورده شده :

Introduction to Data Science

Analyzing and Visualizing Data

Analyzing and Visualizing Data with Power BI
        Analyzing and Visualizing Data with Qlik sense-
            Analyzing and Visualizing Data with tableau-

   Query Relational Data

Querying Data with Transact-SQL-

Explore Data with Code

Introduction to R for Data Science-
Introduction to Python for Data Science-

Plan and Conduct Data Studies

Data Science Research Methods: R Edition-
Data Science Research Methods: Python Edition-

Build Machine Learning Models

Principles of Machine Learning: R Edition-
 Principles of Machine Learning: Python Edition-

Build Predictive Solutions at Scale

Analyzing Big Data with Microsoft R-

Final project

 اساتید دوره آموزشی تربیت کارشناس علم داده



logo