Power BI Training is a data visualization tool that provides a powerful platform for self-service business intelligence. It is a great addition to the skills of any data analyst or aspiring data professional.
A popular method of learning Power BI is through online video tutorials. These lessons are a flexible, affordable option that can be watched on your own time and re-watched as often as you like.
Data modeling is a key skill in Power BI and it’s important to understand how to model your data correctly. Good data modeling allows you to get the most out of your Power BI reports and optimizes performance.
The first step in data modeling is creating a table called a dimension or fact table. A fact table stores quantitative, numerical data and is typically the centerpiece of a Power BI Data Model. The second step is establishing relationships between the tables. Each relationship should have a one-to-one or many-to-one cardinality and a single direction. Bi-directional relationships are often used in Power BI but they’re not recommended and can create a lot of unnecessary complexity in your model.
When establishing relationships, try to only load the columns you need. Having unneeded columns in your Power BI data model bloats the file and can negatively impact performance. This is especially true when establishing relationships between measure tables.
DAX is an aggregation and calculation language that can be used to create calculated columns and measures. It is a powerful tool that allows Data Analysts to find new ways to calculate data values and discover hidden patterns in complex datasets.
DAX expressions are composed of functions, value references, conditional statements, loops, formulas and constants. Every DAX function has an argument, which is the data item being processed. The argument can be a column reference, number, text, other formulas or functions, or a logical value such as TRUE or FALSE.
Each DAX function has its own unique syntax. To begin writing a DAX formula, you must type an equals sign (=) followed by the name of the function. If you’re using a function that requires an argument, you must also enter a parenthesis. For example, the DAX function COUNTROWS requires a column reference to count distinct values in that row. Another example is the DAX function DISTINCTCOUNT, which requires a table context and column context.
Whenever you select a question in the Query Editor, the information it returns is displayed in the Data Preview section and the query’s properties and applied steps are posted on the Query Settings sheet. The Query Settings sheet additionally incorporates gear and exclamation mark symbols that, when clicked, open associated dialog boxes.
Similarly, the M code that Power BI Query generates at each step is shown in the Advanced Editor, giving you added control over your shaping. You can directly alter this code, utilizing the commands available in the ribbon or the M language itself.
The LOOKUPVALUE command uses the DAX function of the same name to look up values in one dimension or table to another, and is a great way to reduce the number of joins in a query. It also works well when combining queries in Power BI. As a rule, it’s best to filter and remove data at an early stage in the query process to limit the number of rows returned.
Visuals are the most important components of a Power BI report as they help you tell your data story. They are responsible for bringing your insights to life by identifying patterns and trends. They are also responsible for allowing users to easily interpret the information within your insight.
There are a variety of visualization options in Power BI, but it is important to choose the right one for the type of information you want to highlight. A bar chart is a popular choice as it allows you to compare multiple categories by size. A scatter chart is another common visualization as it displays the relationship between three variables with the size of each dot representing the variable’s value.
Lastly, a matrix is another visualization option that displays data in a grid format. This is especially useful when you are trying to compare different sets of data and want to display both a horizontal and vertical dimension to the results.