Writing Analytical Queries for Business Intelligence
€1640, aangeboden door Master it Training
In deze 3 daagse training leer je om TSQL queries te schrijven voor database reporting, analysis en business intelligence doeleinden. Deze training presenteert TSQL specifiek binnen de context van data analysis. Met andere woorden leer je hoe de data geïnterpreteerd wordt en niet zozeer de application development kant.De training start met een korte bespreking van de measurement levels en kwantitatieve research methodes en integreert deze concepten in elk afzonderlijk TSQL onderwerp. Het doel is om een consistente, directe en doelgerichte leerweg te bieden voor RDBMS data retrieval voor het gebruiken in analytical tools zoals SQL Server Reporting Services, PowerBI, R en Excel.Na het afronden van deze training kun je: Onafhankelijke en afhankelijke variabelen en measurement levels identificeren in hun eigen analytische werkomgeving Belangrijke variabelen identificeren in relationele database tables Kies een data aggregatie level en data set design geschikt voor de bedoelde analysis en tool Gebruik TSQL SELECT queries om readytouse data sets te produceren in tools zoals PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS en anderen. Het creëren van stored procedures, views en functies om data retrieval codes onder te verdelen
Let op. Deze training is exclusief het examen.
Doelgroep
Deze training is bedoeld voor informatie specialisten en data science professionals die database reporting en analysis tools zoals Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS en andere business intelligence tools willen gaan gebruiken en TSQL queries willen gebruiken om efficiënt data sets terug te halen van de relatiedatabase van Microsoft SQL Server voor het toepassen met deze tools.
Voorkennis voor de Writing Analytical Queries for Business Intelligence training
Voor het volgen van deze cursus dien je te beschikken over de volgende kennis: Context van data analysis en business intelligence scenarios. Bijvoorbeeld kennis van een werk gerelateerd business intelligence project. Basiskennis van het Windows operating system en de core functionaliteiten, inclusief navigatie door de documentopslag. (including file system navigation) Inzicht in het doel van relatie database management systemen zoals SQL Server.
Onderwerpen in de Writing Analytical Queries for Business Intelligence training
Module 1: Introduction to TSQL for Business IntelligenceThis module discusses writing analytical queries vs. transactional DML queries, and describes the typical architecture of a business intelligence environment. It discusses the role of SELECT queries in retrieving data for analysis from relational databases. It introduces the sample database to be used in the course, and begins a presentation of the SELECT query.Lessons Two Approaches to SQL Programming TSQL Data Retrieval in an Analytics / Business Intelligence Environment The Database Engine SQL Server Management Studio and the CarDeal Sample Database Identifying Variables in Tables SQL is a Declarative Language Introduction to the SELECT QueryLab 1: Introduction to TSQL for Business Intelligence Create a database diagram Create and execute basic SELECT queriesAfter completing this module, students will be able to: Describe the purpose of analytical queries Describe the function of TSQL data retrieval in an analytics / business intelligence environment Describe the primary functions of the database engine Discuss TSQL as a declarative language Identify variables of interest in database tables Write basic SELECT queriesModule 2: Turning Table Columns into Variables for Analysis: SELECT List Expressions, WHERE, and ORDER BYThis module covers the identification of and relationship between levels of measurement and column data types. It continues a discussion of the SELECT query and adds the WHERE and ORDER BY clauses.Lessons Turning Columns into Variables for Analysis Column Expressions, Data Types, and Builtin Functions Column aliases Data type conversions Builtin Scalar Functions Table Aliases The WHERE clause ORDER BYLab 1: Write queries using: Column and table aliases DISTINCT WHERE ORDER BY Builtin functions Explicit and implicit data type conversionAfter completing this module, students will be able to: Implement column expressions in SELECT queries Implement column and table aliases Describe data types and Implement data type conversions Implement builtin functions Use WHERE and ORDER BY clauses in SELECT queriesModule 3: Combining Columns from Multiple Tables into a Single Dataset: The JOIN OperatorsModule 3 discusses creating single datasets for analysis by combining results from multiple database tables using JOIN.Lessons Primary Keys, Foreign Keys, and Joins Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product Understanding Joins, Part 2: The INNER JOIN Understanding Joins, Part 3: The OUTER JOINS Understanding Joins, Part 4: Joining more than two tables Understanding Joins, Part 5: Combining INNER and OUTER JOINs Combining JOIN Operations with WHERE and ORDER BYLab 1: Write SELECT queries using: Inner join Left, right, and full join Joins of more than two tables Join operators, in addition to WHERE and ORDER BYAfter completing this module, students will be able to: Discuss and describe the conceptual flow of JOIN operations Implement INNER and OUTER JOIN operations on two or more tables Describe the order of operations of SELECT clauses Combine JOIN operations with WHERE and ORDER BYModule 4: Creating an Appropriate Aggregation Level Using GROUP BYThis module covers the aggregation of quantitative column values across grouping factors for the purpose of groupwise comparisons and/or changing the granularity of a dataset.Lessons Identifying required aggregation level and granularity Aggregate Functions GROUP BY HAVING Order of operations in SELECT queriesLab 1: Write queries using: Aggregate functions Aggregate function with HAVING Aggregate function with GROUP BY and HAVING Aggregate function with GROUP BY, HAVING, WHERE, and ORDER BYAfter completing this module, students will be able to: Describe row granularity of result sets Discuss and implement aggregate functions to achieve required row granularity Use GROUP BY to calculate aggregate values for groups Use HAVING to filter records in the result set by aggregate value Combine GROUP BY and HAVING with WHERE and ORDER BYModule 5: Subqueries, Derived Tables and Common Table ExpressionsThis module covers the use of subqueries, derived tables, and common table expressions in SELECT queries as techniques for creating intermediate result sets.Lessons Noncorrelated and correlated subqueries Derived tables Common table expressionsLab 1: Write queries using: Noncorrelated subqueries Correlated subqueries Derived tables Common table expressions Subqueries, derived tables, and common table expressions in combination with other topics covered in previous modulesAfter completing this module, students will be able to: Describe and discuss the rationale of creating intermediate results sets within SELECT queries Implement noncorrelated and correlated subqueries Implement derived tables Implement Common Table Expressions Create intermediate to advanced TSQL queries to retrieve result sets for analysisModule 6: Encapsulating Data Retrieval LogicThis module discusses the encapsulation of data retrieval logic in views, tablevalued functions, and stored procedures. It also describes scenarios in which these techniques are useful for producing datasets for analysis. Finally, it describes the database security issues involved, and techniques for creating and using these database objects while maintaining current permission sets on source data.Lessons Views Tablevalued functions Stored procedures Creating objects for readaccess users Creating database accounts for analytical client toolsLab 1: Encapsulating Data Retrieval Logic Create a SQL login Create a database user and assign required permissions Create a database schema for views, functions, and stored procs Create a view Create a tablevalues function Create a stored procedure Allow a user with readonly access to use views, tablevalued functions, and store proceduresAfter completing this module, students will be able to: Identify scenarios in which views, tablevalued functions, and stored procedures simply data retrieval Compare and contrast views, tablevalued functions, and stored procedures Create views, tablevalued functions, and stored procedures Describe the security requirement for creating database objects Implement views, tablevalued functions, and stored procedures for users with readonly access to source dataModule 7: Getting Your Dataset to the ClientThis module covers common techniques for making datasets produced by SELECT queries available to analytical client tools such as SQL Server Reporting Services, PowerBI, Excel, and R. It discusses running queries directly from the client tool, in addition to exporting datasets to text files which can then be accessed by the client tool.Lessons Connecting to SQL Server and Submitting Queries from Client Tools Connecting and running SELECT queries from: Excel PowerBI RStudio Exporting datasets to files using Results pane from SSMS The bcp utility The Import/Export WizardLab 1: Getting Your Dataset to the Client Retrieving the results of a view in Excel Running an adhoc SELECT query from Excel Running an adhoc query from PowerBI Running an adhoc query from RStudio Using the Import/Export wizard to write the results of a query to a text fileAfter completing this module, students will be able to: Describe the properties of database connection strings Run queries from, and return results to, Excel, PowerBI, and RStudio Export query results to external text files using the SSMS results pane, the bcp utility, and the Import/Export Wizard