Course 55232: Writing Analytical Queries for Business Intelligence

This three-day instructor led course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence. Specifically, this course presents
TSQL within the context of data analysis – in other words, making meaning from the data rather than transaction-oriented data-tier application development.

The course starts with a brief discussion of levels of measurement and quantitative research methodogy, and integrates these concepts into each TSQL topic presented. The goal is to provide a consistent, direct, and purposeful learning path for RDBMS data retrieval for use in analytical tools such as SQL Server Reporting Services, PowerBI, R, and Excel.This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools.At Course Completion:
Identify independent and dependent variables and measurement levels in their own analytical work scenarios; Identify variables of interest in relational database tables; Choose a data aggregation level and data set design appropriate for the intended analysis and tool. Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others.Create stored procedures, views, and functions to modularize data retrieval code.

About this course:

  • Audience(s): Information workers
  • Technology: SQL Server
  • Level: 200
  • This Revision: A
  • Delivery method: Classroom
  • Length: 3 days

Course Outline:

  • Module 1: Introduction to TSQL for Business Intelligence
  • Module 2: Turning Table Columns into Variables for Analysis: SELECT List Expressions, WHERE, and ORDER BY
  • Module 3: Combining Columns from Multiple Tables into a Single Dataset: The JOIN Operators
  • Module 4: Creating an Appropriate Aggregation Level Using GROUP BY
  • Module 5: Subqueries, Derived Tables and Common Table Expressions
  • Module 6: Encapsulating Data Retrieval Logic
  • Module 7: Getting Your Dataset to the Client

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