Writing Analytical Queries for Business Intelligence Training Course (55232-A)

Course 55232-A: Writing Analytical Queries for Business Intelligence Certification Training Course Overview

This course is about writing TSQL queries which is 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 will be starting with a brief discussion of levels of measurement and quantitative research methodology, and integrates these concepts into each TSQL topic presented. The goal of this course is to provide participants 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.

Audience

  • Information Workers and Data Science professionals who prefers to use database reporting and analysis tools like Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and is willing to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools.

Writing Analytical Queries for Business Intelligence Training Course (55232-A) (Duration : 24 Hours) Download Course Contents

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Group Training 1100
15 - 17 Nov GTR 09:00 AM - 05:00 PM CST
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06 - 08 Dec 09:00 AM - 05:00 PM CST
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Course Modules

Module 1: Introduction to TSQL for Business Intelligence
  • Two Approaches to SQL Programming
  • TSQL Data Retrieval in an Analytics 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 Query
  • Lab 1
Module 2: Turning Table Columns into Variables for Analysis: SELECT List Expressions, WHERE, and ORDER BY
  • Turning Columns into Variables for Analysis
  • Column Expressions Data Types and Built-in Functions
  • Column aliases
  • Data type conversions
  • Built-in Scalar Functions
  • Table Aliases
  • The WHERE clause
  • ORDER BY
  • Lab 2- Write queries using:
Module 3: Combining Columns from Multiple Tables into a Single Dataset: The JOIN Operators
  • 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 BY
  • Lab 3- Write SELECT queries using:
Module 4: Creating an Appropriate Aggregation Level Using GROUP BY
  • Identifying required aggregation level and granularity
  • Aggregate Functions
  • GROUP BY
  • HAVING
  • Order of operations in SELECT queries
  • Lab 4- Write queries using:
Module 5: Subqueries, Derived Tables and Common Table Expressions
  • Non-correlated and correlated subqueries
  • Derived tables
  • Common table expressions
  • Lab 5- Write queries using:
Module 6: Encapsulating Data Retrieval Logic
  • Views
  • Table-valued functions
  • Stored procedures
  • Creating objects for read-access users
  • Creating database accounts for analytical client tools
  • Lab 6
Module 7: Getting Your Dataset to the Client
  • 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 Wizard
  • Lab 7
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Course Prerequisites
  • Context knowledge of data analysis and business intelligence scenarios.
  • Basic knowledge of the Windows operating system and its core functionality, including file system navigation.
  • Basic understanding of the purpose of relational database management systems such as SQL Server.

Upon completion of this course, you will be able to accomplish

  • Knowing the methods to identify independent and dependent variables and measurement levels in their own analytical work scenarios.
  • Knowing to 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.
  • To create stored procedures, views, and functions to modularize data retrieval code.

 

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