Turning Data into Insights with Power BI
The course is part of this learning path
Power BI allows business users to analyze data and share insights across all levels of an organization. It gives an end-to-end view of important metrics and key performance indicators through intuitive and interactive dashboards all in one place.
In this course, you will learn about several Power BI tools that would help you enhance reports to expose insights and present them in a way that appeals to a wide range of end-users.
- Understand how to apply conditional formatting, slicers, and filters
- Perform top N analysis
- Explore statistical summary
- Use the Q&A visual
- Add a Quick Insights result to a report
- Understand when and where to create reference lines by using the Analytics pane
- Learn when and where to use the Play Axis feature of a visualization
- Understand how to personalize visuals
- Beginners to data analytics
- Business analysts
- Business intelligence developers
- Business intelligence managers
- Anyone who wants to learn about Power BI
- Microsoft Power BI Desktop for PC/Windows users (free download)
- Familiarity with preparing data using Power BI
- Familiarity with modeling data using Power BI
- A basic understanding of Power Query, Power Pivot, and DAX is a plus but not required
Let's talk about statistical summary. Let's say our district manager asked us to create a report showing the frequency of orders for certain products in that district's market and the average unit sales of these products. Here a statistical summary is a way to provide a quick and simple description of our data. Statistical summary shows us the key distribution of our data and helps us identify key takeaways and anomalies that might exist in our dataset. In this lecture, we will summarize the column within a table and explore key statistical functions using DAX.
So, in order to get started, let's go to Power BI Desktop and connect to our sample report. So, let's use some of the key statistical DAX functions in order to analyze our data set and get a better understanding of our data model. We are in the table view of our report and let's actually analyze our sum of regular sales unit column. Let's go back to our report view, create a new measure here. Unless you use a few statistical functions to understand the sales column, let's type in mean of sales unit and this is going to be set to equal to mean of that regular sales column.
So, this is just a quick way to see the smallest value of that column, throw that in here as a table. Let's expand this a bit. We see that our small sales unit is - $51 indicating a net loss in sales possibly due to exceeding costs or refunds of the units sold. Let's do the exact same thing for max to get an understanding of the largest sum of sales unit and this is going to be set to equal to max of that regular sales column. So, our largest sales on a unit item is about $6,000. We can do the average of sales of units and this is going to be set to equal to average of that regular sales column. So, our average sales on a unit item is actually $8. So, that's interesting to see. And then if you want to get a little bit deeper, we can take the median as well. And this is going to set to equal to median of that regular sales column. So, the average and median don't have to be the same, we see that median is actually $3 showing us that more sales on units are seen for slightly expensive items maybe as opposed to the average.
Since average is going to be weighted based on the actual price of the items, whereas median is going to take into account more than the count of orders placed for those certain items. We can go even deeper if we want to, we can do something like the standard deviation for example, to get a better understanding of our data sets from a statistical point of view. And if you are deep into statistics, I highly recommend to explore all the functions available in DAX. So, in this lecture we summarized a column within a table and explored key statistical functions using DAX. The statistical summary helps us to quickly explore part of a whole data and show these insights in a statistical way in our reports.
Moatasim has been a data and insight consultant since 2014, driving data culture strategies in enterprises, non-profit organizations and tech startups to improve their decision making. He has teamed up with Fortune 1000 companies, MBB and Big Four consultants on complex engagements in government and private sectors. He has been a data analyst, business analyst, BI manager, and instructor. To date, Moatasim has created learning content relating to business intelligence, data analysis and machine learning, mostly within Power BI, Azure, SQL and Python. His hobbies included heavy metal drumming and meditation.