Microsoft Power BI - Up & Running With Power BI Desktop

In this section we'll cover how to download Power BI Desktop and configure options and regional settings to make sure you're working with the same Power BI setup.

In this section I'll discuss some of the resources that I find most helpful, like the "Help" section in Power BI Desktop, the Microsoft Power BI blog, and the Microsoft Power BI YouTube channel.

In this lecture we'll introduce the new Power BI Desktop ribbon interface, introduced as a preview feature in November 2019 and rolled out to general availability in early 2020.

In this lecture we'll kick things off and cover the robust data connection library where you can connect to just about any data source. I'll highlight some of the available connectors and walk through the Get Data dialogue box.

The Query Editor is your cockpit for loading and transforming raw data in Power BI Desktop. In this lecture we'll take a tour of the tools and features that will help us transform and shape our data.

In this lecture we'll learn about some of the most important table transformation tools and options in Power BI Desktop, including promote header row, duplicate, move, and rename columns, and keep or remove rows.

In this lecture we'll review Power BI Desktop tools designed specifically to work with numbers or data fields, such as returning aggregated values like sums or averages, creating new calculated columns, rounding numbers, etc.

In this lecture we'll cover Power BI Desktop tools specifically designed to work with date and time fields, like calculating months, weeks, weekdays, quarters, etc.

In this lecture I'll quickly demonstrate how you can use custom M queries to build a rolling calendar in Power BI Desktop that will always update with dates through the current day.

This lecture will show you how to create new unique identifiers using index columns in Power BI, as well as calculated fields based on custom user-defined conditions.

In this lecture we'll practice using "Group By" tools in the Transform tab of the Query Editor to aggregate or roll up raw data to new levels of granularity.

This lecture demonstrates how to merge queries together within Power BI Desktop to pull fields from one table into another based on common fields or "keys".

In this lecture, I'll show you how to append or "stack" data from multiple Power BI queries that share the same column structure and data types.

The Data Source Settings in the Query Editor allows you to manage data source connections and permissions.

In this lecture, we'll cover how to customize your Query Refresh settings as well as refresh all queries.

In this lecture, we'll explore the Power BI "Modeling" tab in the Data view, where I'll show you how to edit field properties to define specific categories.

In this lecture we'll practice creating hierarchies in our Power BI model, which are groups of nested columns that reflect multiple levels of granularity (such as country, state, and city).

In the PRO TIP lecture, I'll show you how to import a fully built data model from Excel into Power BI Desktop.

In this lecture, I'll share some of the best practices, like organizing your datasets and establishing a folder/file structure, when connecting and shaping data in Power BI Desktop.

A data model is a group of tables that are connect by a common field or "key". In this lecture, I'll explain the core concept of joining Power BI datasets together with common keys.

This lecture covers one of the most important topics in the course: database normalization. Normalization is all about structuring tables in a way where each table serves a distinct and specific purpose.

In this lecture we'll compare and contrast the two primary types of tables in a Power BI model: data (or "fact") tables and lookup (or "dimension") tables.

In this lecture I'll explain the difference between manually merging fields from multiple tables and creating relationships to connect them using the Power BI data model (which is much more efficient!)

In this lecture we'll use Power BI Desktop to create our first table relationships.

The Manage & Edit Relationships dialogue allows you to edit, add, or delete table relationships within Power BI Desktop.

In this lecture I'll demonstrate how to create multiple relationships against the same key, and how to determine which relationship is active vs. inactive in Power BI Desktop.

In this lecture I'll demonstrate exactly how to build a Power BI model containing multiple data tables. Rather than connect those tables together, we'll connect them indirectly via relationships to shared lookup tables.

This lecture demonstrates the importance of filter direction within a Power BI data model, and explains the concept of filter context flowing "downstream" to related tables.

In this lecture I'll cover examples of how, and when, to use two-way filters (bi-directional filtering) in Power BI Desktop. This technique allows filter context to flow from dimension to fact tables and from dimension to fact tables.

In this lecture, I'll cover how, and when, to hide fields from your Power BI Report view.

In this lesson I'll walk you through the updated "Model" view in Power BI Desktop, which includes tools to quickly edit model properties, organize fields, and create custom views based on subsets of your full data model.

In this lecture I'll outline my personal favorite Power BI data modeling best practices.

Meet DAX, or Data Analysis Expressions, the formula language that drives Power BI.

In this lecture I'll introduce the first method of using DAX to create new calculated fields: calculated columns. I'll showcase some "good" and "bad" examples, and demonstrate how they can be created within the data model window.

In this lecture I'll introduce the second method of using DAX to create new calculated values: measures. I'll explain how they can be used and why they are so powerful in Power BI Desktop.

In this lecture, I'll cover two options for adding columns and measures in Power BI, and highlight the important differences between the two options.

In this lecture, I'll cover the difference between implicit measures, which are measures that are automatically created by Power BI Desktop when you drag raw numerical fields into the visuals pane, and explicit measures, which are created by entering DAX functions.

In this lecture I'll cover filter context, which allows us to understand exactly how each "cell" is being calculated, and how it impacts DAX measures. 

In this lecture I'll walk through DAX formula syntax and outline the most common types of operators.

In this lecture I'll outline some of the most common DAX categories in Power BI (Math & Stats, Logical, Text, Filter, and Date & Time), as well as example functions.

In this lecture, I'll cover how to add calculated columns with basic date and time functions like WEEKDAY, TODAY, and DATEDIFF.

In this lecture I'll introduce and demonstrate several common logical functions in DAX, including IF, IFERROR, AND, OR, etc.

In this lecture I'll introduce and demonstrate a number of text-specific functions in DAX, including LEN, CONCATENATE, UPPER/LOWER, LEFT/MID/RIGHT, SEARCH, and SUBSTITUTE.

In this lecture I'll show you how to use the RELATED function to create new calculated columns that retrieve values from related tables in the Power BI data model.

In this lecture I'll introduce and demonstrate several common math and statistics functions in DAX, such as SUM, DIVIDE, MAX, MIN, and AVERAGE.

In this lecture I'll demonstrate how to use a variety of COUNT functions in DAX, including COUNT, COUNTA, COUNTROWS, and DISTINCTCOUNT.

In this lecture, I'll discuss an important characteristic of our sample transaction data, and discuss how it impacts our reporting views.

In this lecture I'll explain how to use the ALL function to remove filter context, and demonstrate how it is commonly used for "% of whole" calculations defined by DAX measures.

In this lecture I'll explain how to use the FILTER function to return a table that represents a subset of another table, and demonstrate how FILTER can be used within the CALCULATE function.

In this lecture I'll introduce you to iterator, or "X" functions, which operate by repeating a calculation across all rows in a table and aggregating the results. In this demo we'll look at the SUMX function specifically.

In this lecture we'll introduce DAX's powerful time intelligence formulas, and demonstrate how to use them to measure performance-to-date, make period-over-period comparisons, and calculate running totals and moving averages.

In this lecture I'll outline some of my personal favorite DAX best practices.

In this lecture we'll discuss the panes, toolbars, and options available within the Power BI Desktop "Report" view.

In this lesson, we'll review the basic ways to add objects and charts into the Power BI Report view, via the Home tab or visualizations pane, and kickoff building our Adventure Works report.

In this lecture I'll walk through adding our first chart to the Power BI Report canvas (a stacked bar chart), and cover the fields and formats options available for this specific chart type.

In this lecture I'll walk through conditional formatting options in the newer versions of Power BI Desktop (post Nov 2018)

In this lecture, I'll cover Power BI report formatting options, which is all about customizing the look and feel of the visualizations, and highlight formatting options for Line & Column Chart, Matrix, and Donut Chart types.

I'll cover the incredibly powerfull filtering options within Power BI and outline the four primary filter types: Visual Level, Page Level, Report Level, and Drillthrough.

In this demo, I'll focus on the Matrix visualization type and customize the visual to make it easily interpretable. 

In this demo, we'll create a Date Slicer and use it to filter the views in our Power BI report.

In this demo I'll cover how card visuals can be used for text-based fields (not just values) in Power BI, and how to format cards in a clear and effective way.

In this lesson, we'll introduce Power BI Desktop's powerful, flexible, and user-friendly mapping tools.

In this demo, I'll cover one of the most underrated chart types in Power BI: the Treemap. Treemaps are a very useful way to show composition and provide a very quick picture of relative volume and proportion. 

In this demo, I'll show you how to visualize trending using line and area charts in Power BI Desktop.

In this demo, I'll cover how to add trend and forecast lines from the analytics pane, and apply them to a line chart in the Power BI Report view.

In this lesson we'll cover a very important topic in Power BI Desktop: report interactions. Report interactions allow you to determine exactly how filters applied to one visual impact the other visuals in your report canvas.

In this lecture we'll introduce Drillthrough filters. This filter type allows users to jump to different report pages in Power BI (like bookmarks), while simultaneously filtering based on the specific item selected. 

In this lecture we'll cover "What-If" Parameters, which are essentially pre-set measures that produce values within a given range, based on user-inputs like data type, min/max, increment, and default.

I'll use this lecture to cover managing and viewing roles in Power BI Desktop, two features found in the Security tool group of the Modeling tab. These features allow you to define filtered views tailored to specific audiences or user groups.

In this lecture we'll preview how to access and load custom visuals within Power BI Desktop.

In this lecture we'll preview how to design Power BI reports specifically for mobile viewing.

In this lecture we'll quickly preview how to publish a report from Power BI Desktop to Power BI Service.

In this lecture I'll cover some of my top Power BI data visualization best practices. 

In this Power BI demo we'll practice using the Q&A visual to explore our data using natural language queries.

In this demo we'll explore the Power BI Key Influencers visual, and use it to predict whether or not a Kickstarter project will be successful.

In this demo we'll explore the Power BI Key Influencers visual, and use it to understand which specific factors impact the amount of money a Kickstarter project is expected to raise.

In this demo we'll address one of the most important rules in data science and analytics: correlation does not imply causation. We'll discuss why this concept is so important to grasp when exploring AI-powered visuals like the Key Influencers visual in Power BI.

In this Power BI demo we'll explore the Decomposition Tree visual, and use it to explore the distribution of our sample data and conduct a simple root cause analysis.

Complete the full stack of Excel, Power BI, and SQL analytics courses and emerge a certified wizard. Courses include Formulas & Functions, PivotTables, Charts & Graphs, Power Query, Power Pivot & DAX, Publishing to Power BI Service, and MySQL for Data Analysis (Beginner & Advanced).

In this lecture, I'll be kicking off the bonus course project where you'll be working through the entire business intelligence workflow for a new client, Maven Market.

In part 1 of the bonus course project you'll be connecting and shaping Maven Market's data.

In part 2, you'll be using the report created in part 1 to build a data model.

In part 3, you'll be adding all the DAX measures for use in your final report.

In part 4, you'll build the final report visual for Maven Market.