Microsoft Power BI is a suite of Business Intelligence tools, designed to help the BI professionals get easier, quick, and crucial business insights from their data. It contains three main tool-set combined in one single software:

• Power Query
• Power Pivot
• Power View

Power Query lets you connect to hundreds of data sources, simplify data preparation, and drive ad-hoc analysis. It is also known as Power BI Query Editor.

Power Pivot is the analytical tool in Power BI. Using various DAX formulas you can drive your analytical journey. This is the second step after you import the data into Power BI using Power BI Query Editor.

This course will focus completely on features for Power BI Query Editor, Date Modelling, and DAX inside Power BI Desktop.

Before you can present any analysis or insight, you need source data. Your source data could be in many places and in many formats. Nonetheless, you need to access it, look at it, and quite possibly clean it up to some extent. You may also need to join separate data sources before you can shape the data into a coherent data set using PowerPivot, deliver the results using Power View or Power Map, and then share it using Power BI.

Discovering, loading, cleaning, and modifying source data is where Power BI Query editor comes in. It lets you load, shape, and streamline data from multiple sources.

Power BI Query Editor allows you to do many things with source data, but the four main steps are likely to be

• Import data from a wide variety of sources. This covers corporate databases to files and social media to big data.
• Merge data from multiple sources into a coherent structure.
• Shape data into the columns and records that suit your uses.
• Cleanse your data to make it reliable and easy to use.

There was a time when these processes required dedicated teams of IT specialists. Well, not anymore. With Power Query, you can mash up your own data so that it is the way you want it and is ready to use as part of your self-service BI solution.

This course will start with the basic installation and configuration of Power BI Desktop, and go on to connect your data sources with it. You’ll transform and get your data ready for analysis, and create effective data views using it. You would be performing the following tasks mainly

• Data Discovery —Find and connect to a myriad of data sources containing potentially useful data. This can be from both public and private data sources.
• Data Loading —Select the data you have examined and load it into Power Query for shaping.
• Data Modification —Modify the structure of each data table that you have imported, filter, and clean the data itself, and then join any separate data sources.

Although I have outlined these three steps as if they are completely separate and sequential, the reality is that they often blend into a single process. Indeed there could be many occasions when you will examine the data after it has been loaded into Power Query—or join data tables before you clean them. The core objective will, however, always remain the same: find some data and then load it into Power Query where you can tweak, clean, and shape it.

This process could be described simplistically as “First, catch your data.” In the world of data warehousing, the specialists call it ETL, which is short for Extract Transform Load. Despite the reassuring confidence that the acronym brings, this process is rarely a smooth logical progression through a clear-cut series of processes. The reality is often far messier than that. You may often find yourself importing some data, cleaning it, importing some more data from another source, combining the second data set with the first one, cleaning some more, and then repeating many of these operations several times.

This course will excite and empower you to get more out of Power BI Query Editor via detailed recipes, development tips, and guidance on enhancing existing Power BI Projects.

Related Courses:

Power BI For Data Visualization And Analysis