The learning path Power BI started in april 2017 when I did my first Analyzing and Visualizing Data with Power BI online training in edX. At that moment my passion for Power BI was born.
Overtime I have been experimenting with Power BI and during my Data Science Professional Microsoft Track powered by Techionista learned more about Power BI.
In the past weeks I have been busy with exploring Power BI, by just diving into #MakeOverMonday challenges and working with trial and error. This way of working helped me to understand the program, but it also fed my hunger in getting to know more about Power BI.
In my search for video material I came across Avin Singh, he has a nice video series where he starts from the beginning with introducing Power BI. In this blog are my learning notes. I do advise you to watch the video series to understand Power BI better.
Before I started with this series I started with the most basic one “how to install Power PI”.
In this video he talks about the two way’s to install Power BI
- From the Microsoft website
- From the Microsoft store
My Power BI was installed from the Microsoft website and the plus point that you do not have to update it every month yourself, made me decide to deinstall that version and to install Power BI from the store. Within an hour I went back to the website version. I wanted to change something in the settings and I could not. I kept giving me a popup error message. I did not want to figure out way and decide that the website version and updating it manually every month work well in the past and I would keep it like that.
In Get Data he explains how to import data and how you can edit it with the query editor and how the query editor helps you with making a documentation so that somebody else (or yourself after some time) knows what has happened. Good documentation is important in Data Science, because in that why you can explain and prove to others what you have done.
In Relationships he tells you how to make a good data model. He explains about data tables and fact tables.
Up to know repeating of the knowledge I got from earlier training and self-exploring of Power BI.
In the section about DAX I get to learn some new facts.
A calculated column is a nice way for the human brain to see in the tables what is happening. It makes the file grow bigger in size.
We want to have a small file, because a Power BI file runs in memory of the computer.
The storage of a big file is not a problem, running a big file in memory can give a problem.
So it is better to use a measure. This does not make the file bigger. To write them you need to have more DAX knowledge. In his video’s he explains the basics.
There are two types of measures “Implicit” and “Explicit”.
Implicit means you take the column from the table and put it in the visualisation.
Explicit measure is a measure you made by using a DAX-formula.
He explains them and tells witch one is better to be used. To make that clear he really gives a nice picture why you are building a Power BI file.
The Data Scientist is the author from an Excel or a Power BI file, that file is being published in the cloud of Power BI and becomes the only file of truth. That file can be made excisable for consumers to use the data with the program they want.
And at that point it becomes very important if you have use implicit or explicit measures. Implicit measure can not be put in a pivot table in Excel, while explicit can.
A lot of users use Excel, so you need to build for their needs.
He tells a lot more about DAX measures and I start to understand it.
At the end he creates a report, that turns out to be a dashboard and online published. I did make the report he made, but did not publish it. I also changed how to put the text and images. I first made a jpg by using PowerPoint and loaded that as the background of the report (tip from Marc Lelijveld during a webinar I followed in July 2019)
Here is the created visual.