Analyzing Data with Power BI, DAX, and Power Query M Seminar offered by Hands-On Technology Transfer

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Analyzing Data with Power BI, DAX, and Power Query M
Hands-On Technology Transfer

Delivery Method
Seminar
Seminar

Target Audience
Power BI users who want to understand DAX and Power Query M

Summary
This course provides a robust and in-depth introduction to Microsoft's Power BI suite of products and gives students a solid understanding of data analysis with Power BI, DAX, and Power Query M

Description/Agenda

This course provides a robust and in-depth introduction to Microsoft® Power BI suite of products and gives students a solid understanding of data analysis with Power BI, DAX, and Power Query M. Students will learn to use the Power BI Desktop and Power BI online service to import, analyze, and visualize business data, and to share business intelligence. In addition, students will learn beginner and intermediate techniques for adding calculations to their Power BI Data models using DAX, as well as how to use the Power Query M language to write advanced queries in order to populate a data model from external data sources.

Topics include an exploration of the evolution of relational databases, data warehouse and business intelligence; importing and combining data from diverse data sources; shaping and transforming data; modeling data; developing useful and insightful visualizations; creating and sharing reports and dashboards in the Power BI service; collaborating in the Power BI service; using DAX to solve common data modelling problems; how to flatten out an OLTP database into a star schema by using DAX; resolving common granularity issues with data models; and using Measures to solve advanced calculation problems that languages like SQL are unable to tackle.

Students who want Power BI training but do not need detailed coverage of DAX and Power Query M should instead attend the shorter Analyzing and Presenting Data with Power BI course rather than this course.

Course Prerequisites

Before attending this course, students must have:

High degree of computer literacy
Experience with office applications
Familiarity with spreadsheet functionality
An understating of fundamental business analysis issues such as revenue, profitability, financial accounting, and reporting
Basic familiarity with relational databases and data warehousing is helpful but not required

What Students Will Learn

The fundamentals and evolution of business analysis processes and tools
The roles, tasks and tools involved in self-service business analysis
How to connect to and import data from numerous diverse sources into the Power BI Desktop and service
When to directly query data sources and when to import data
How to shape, transform, and combine data from diverse sources to create useful reports
Using the Power Query Editor
Which visualizations are best suited for various types of analysis
How to create, organize and format visualizations that evoke insight and deliver optimal impact
How to use and develop data models and related tools to combine datasets for analysis
How to modify relationships, cardinality, and cross filter direction to maximize the efficacy of analysis
Publishing to the Power BI service
Create and manipulating dashboards, reports and apps in the Power BI service
Refreshing datasets, reports and dashboards in the Power BI Desktop and service
Sharing reports, dashboards, workbooks and datasets in the Power BI service
Creating hierarchical drilldown reports
Collaborating in the Power BI service
Using DAX to add new Calculations to an existing data model
Simulating a Star data Schema from an OLTP source with DAX
Fixing Data Granularity issues with DAX Calculations
Providing Row Level security using Row Filters in DAX
Performing Transformations while loading data with Power Query and M
Using M language to write more powerful queries than the standard Power Query user interface allows

Workshop Agenda

Introduction to Business Analysis The Evolution of Data Analysis Databases Normal Forms Data Modeling Data Processing Beyond the Database Data Warehouses Fact and Dimension Tables Data Warehouse Schema Star Schema Snowflake Schema Big Data Self-Service Business Intelligence Enterprise Intelligence Self-Service BI The BI Team The Data Steward The Tasks Accessing Data Loading Data Shaping Data Reporting Visualizing Data Collaborating The Tools The Power BI Suite Related Tools	Getting Data with Power BI Desktop The Power BI Workflow Terminology Opening the Power BI Desktop The Navigation and Filter Panes The Visualizations Pane The Fields Pane Power BI Desktop Settings Data Sources On-Premises Data Files Databases Online Databases Software as a Service (SaaS) Providers Internet Data Data Access Issues Data Reliability Issues Getting Data Import vs. Direct Query Advantages of Direct Query Limitations of Direct Query Data Limits Cleaning Data Selecting Data Fundamentals of Getting Data Importing from Excel Preparing Excel Data Importing from Excel Importing from a Database Selecting Data with SQL Importing from a Web Page Revising Data
Transforming and Shaping Data Getting the Right Data Shaping Data How and Where to Transform Data Formatting Currency Columns and Measures Filters The Power Query Editor How Power BI Accesses and Stores Data Query Editor Layout The Transform Tab The Add Column Tab The View Tab Manipulating Columns Applied Steps The Advanced Query Editor	Visualizing Data Visual Data Processing Why We Visualize Choosing the Right Visualization Report Design Creating Visualizations Basic Layout Selecting Data Starting with Data Starting with Visualization Type Sorting Adding Data, Changing Visualizations The Slicer Fields, Format and Analytics in the Visualizations Pane Formatting Visualizations General Formatting Formatting Groups Title Report Themes Data Colors Data Labels Data Labels Display Units Axes Text Boxes, Images, Shapes Visualization Mechanisms The Slicer Q&A Custom Visuals
Types of Visualizations Basic Visualizations Tables Matrixes Bar/Column Charts Clustered Stacked 100% Ribbon Charts Waterfall Charts Line/Scatter/Area Charts Line Chart Scatter Chart Area Charts Line-and-Column Charts Pies and Donuts Pie Charts Donut charts Map Charts Specialized Charts Funnel Chart Gauge Chart Card Chart Multi-Row Card KPI Key Influencers Tree Maps	Modeling Data Data Models The Utility of Data Models Data Types Cardinality Cross Filter Direction Relationships Understanding Relationships Relationships and Keys Hierarchies The Utility of Hierarchies Creating Hierarchies The Model View Changing Relationships Using the Model View
The Power BI Service Overview Functions by Application Limitations Connecting to the Power BI Service Publishing a Report to the Power BI Service Direct Connectivity from the Power BI Service Refreshing Data Gateways The Power BI Service Interface Layout My Workspace Report Edit Mode Dashboards Dashboard Foundations Dashboards vs. Reports Building Dashboards Editing a Dashboard Filtering a Tile Working the Power BI Service Reports Exporting Reports and Data Workbooks Datasets Apps Sharing Reports and Dashboards Q&A Featured Questions Power BI Service Settings Administration Power BI Administration Admin Portal Power BI Mobile The App To Set Up Your Smartphone Layout Power BI Mobile Features and Limitations	Power BI and Programming Languages Survey of Power BI Languages Why? The Power BI REST API R M Python DAX Data Analysis Expressions DAX Syntax Logical Operators DAX Examples
DAX Fundamentals DAX Capabilities and Common DAX Use Cases DAX Formula Syntax Fundamentals Formula Evaluation Order DAX Operators DAX Operator Precedence DAX Data Types DAX Type Conversions DAX Functions Nested Function Execution Order Naming Requirements	Basic DAX Calculations Calculated Columns Evaluation Context and Row Context Working with Relationships Using RELATED() and RELATEDTABLE() Using Ad-hoc Relationships with USERELATIONSHIP() Flattening Models with Calculated Columns Calculated Tables
Advanced DAX Calculations Measure Use Cases Implicit Measures Calculated Measures Evaluation Context and Filter Context Implicit Filter Context Explicit Filter Context Filtering with Calculate() and Filter() Undoing Filters with All() Iterator Fundamentals Working with built-in Iterator Functions Using Nested Iterators	DAX and Modeling Resolving Model Granularity Issues Flattening Models Row Level Security with Row Filters Avoiding Ambiguous Relationships with Role Playing Tables
M and Power Query Power Query Basics Using the Formula Bar in Power Query Using the Advanced Editor in Power Query PowerQuery® Standard Function Library M Language Case Sensitivity in Power Query and M Single Literal Values Intrinsic Values Structured Values: Lists, Records, and Tables M Language Operators Commenting Code in M Using the Let Statement Variable Naming in M M’s Each Statement Creating Columns in M Conditional Constructs in M M Functions

Course Benefits

Course materials include student guide, hands-on lab manual and USB flash drive for examples and lab work
Students receive a certificate of completion at the end of class
Students can retake any portion of a live class that has been completed, within 12 months at no extra cost
There are no registration fees or cancellation fees

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