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Data Analytics Certificate

About this course

This course covers Microsoft SQL Databases Development and the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will show how to access and process data from a range of data sources including both relational and non-relational sources. Finally, this course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution.

Students are expected and prepared to take and Pass Exam PL-300: Microsoft Power BI Data Analyst.

Instructor-Led Virtual Live Class

Students are expected to take two courses during this data Analytics training. The courses are:

  • Developing SQL Databases
  • Design and Manage Analytics Solutions Using Power BI

20761 Querying Data with Transact SQL - 40 Hours

COURSE OUTLINE

1. INTRODUCTION TO MICROSOFT SQL SERVER
  • Learn the Basics of SQL Server Architecture
  • Get Exposed to the Different SQL Server Editions and Versions
  • Become Familiar with SQL Server Management Studio
  • Lab: Working with SQL Server Tools
  • Learn the Components of T-SQL
  • Understand Sets and Predicate Logic
  • Master the Logical Order of Operations in SELECT statements
  • Lab: Introduction to Transact-SQL Querying
  • Write Simple SELECT Statements and CASE Expressions
  • Eliminate Duplicates With DISTINCT
  • Practice Using Column and Table Aliases
  • Lab: Writing Basic SELECT Statements
  • Understand Joins
  • Query with Inner Joins, Outer Joins, Cross Joins and Self Joins
  • Lab: Querying Multiple Tables
  • Learn How to Sort Data
  • Filter Data with Predicates
  • Work With Unknown Values
  • Lab: Sorting and Filtering Data
  • Learn About SQL Server Data Types
  • Work With Character Data and Date and Time Data
  • Lab: Working with SQL Server Data Types
  • Insert Data
  • Modify and Delete Data
  • Lab: Using DML to Modify Data
  • Write Queries with Built-In Functions
  • Practice Conversion Functions and Logical Functions
  • Practice Using Functions to Work with NULL
  • Lab: Using Built-In Functions
  • Use Aggregate Functions and the GROUP BY Clause
  • Filter Groups with HAVING
  • Lab: Grouping and Aggregating Data
  • Write Self-Contained and Correlated Subqueries
  • Execute the EXISTS Predicate with Subqueries
  • Lab: Using Subqueries
  • Navigate Views and Inline Table-Valued Functions
  • Manipulate Derived Tables and Common Table Expressions
  • Lab: Using Table Expressions
  • Complete Queries with the UNION Operator
  • Use EXCEPT, INTERSECT and APPLY
  • Lab: Using Set Operators
  • Create Windows with OVER
  • Explore Window Functions
  • Lab: Using Windows Ranking, Offset, and Aggregate Functions
  • Write Queries with PIVOT and UNPIVOT
  • Work with Grouping Sets
  • Lab: Pivoting and Grouping Sets
  • Query Data with Stored Procedures
  • Pass Parameters to Stored procedures
  • Create Simple Stored Procedures
  • Navigate Dynamic SQL
  • Lab: Executing Stored Procedures

Analyzing Data with Power BI – 30 hour

COURSE OUTLINE

1. Explore the analytics process that turns data into insights
  • Types of data analytics
  • Roles in data analytics
  • Core analytics tasks
  •  
  • Use Power BI
  • Building blocks of Power BI
  • Tour and use the Power BI service
  •  
  • Explore end-to-end analytics with Microsoft Fabric
  • Explore data teams and Microsoft Fabric
  • Enable and use Microsoft Fabric
  •  
  • Use Copilot in Power BI to prepare and model data
  • Create reports with Copilot in Power BI
  • Get your data ready for AI usage in Power BI

Prepare the data

1. Get or connect to data
  • Identify and connect to data sources or a shared semantic model
  • Change data source settings, including credentials and privacy levels
  • Choose between DirectLake, DirectQuery, and Import
  • Create and modify parameters
  • Evaluate data, including data statistics and column properties
  • Resolve inconsistencies, unexpected or null values, and data quality issues
  • Resolve data import errors
  • Select appropriate column data types
  • Create and transform columns
  • Group and aggregate rows
  • Pivot, unpivot, and transpose data
  • Convert semi-structured data to a table
  • Create fact tables and dimension tables
  • Identify when to use reference or duplicate queries and the resulting impact
  • Merge and append queries
  • Identify and create appropriate keys for relationships
  • Configure data loading for queries

Model the data

1. Design and implement a data model
  • Configure table and column properties
  • Implement role-playing dimensions
  • Define a relationship’s cardinality and cross-filter direction
  • Create a common date table
  • Identify use cases for calculated columns and calculated tables
  • Create single aggregation measures
  • Use the CALCULATE function
  • Implement time intelligence measures
  • Use basic statistical functions
  • Create semi-additive measures
  • Create a measure by using quick measures
  • Create calculated tables or columns
  • Create calculation groups
  • Improve performance by identifying and removing unnecessary rows and columns
  • Identify poorly performing measures, relationships, and visuals by using Performance Analyzer and DAX query view
  • Improve performance by reducing granularity

Visualize and analyze the data

1. Create reports
  • Select an appropriate visual
  • Format and configure visuals
  • Create a narrative visual with Copilot
  • Apply and customize a theme
  • Apply conditional formatting
  • Apply slicing and filtering
  • Use Copilot to create a new report page
  • Use Copilot to suggest content for a new report page
  • Configure the report page
  • Choose when to use a paginated report
  • Create visual calculations by using DAX
  • Configure bookmarks
  • Create custom tooltips
  • Edit and configure interactions between visuals
  • Configure navigation for a report
  • Apply sorting to visuals
  • Configure sync slicers
  • Group and layer visuals by using the Selection pane
  • Configure drillthrough navigation, including pages, filters, and buttons
  • Configure export settings
  • Design reports for mobile devices
  • Enable personalization in a report, including personalized visuals
  • Design and configure Power BI reports for accessibility
  • Configure automatic page refresh
  • Use the Analyze feature in Power BI
  • Use grouping, binning, and clustering
  • Use AI visuals
  • Use reference lines, error bars, and forecasting
  • Detect outliers and anomalies
  • Use Copilot to summarize the underlying semantic model

Manage and secure Power BI

1. Create and manage workspaces and assets
  • Create and configure a workspace
  • Configure and update an app
  • Publish, import, or update items in a workspace
  • Create dashboards
  • Choose a distribution method
  • Configure subscriptions and data alerts
  • Promote or certify Power BI content
  • Identify when a gateway is required
  • Configure a semantic model scheduled refresh
  • Assign workspace roles
  • Configure item-level access
  • Configure access to semantic models
  • Implement row-level security roles
  • Configure row-level security group membership
  • Apply sensitivity labels