Data Analysis in Power BI for Beginners

To turn raw data into insights, analyzing the data is crucial. Power BI provides plenty of tools for effective data analysis. From this blog, we will learn about various techniques to analyze data in Power BI. This blog will cover the following topics.

• Introduction to Power BI
• What is Data Analysis?
• Explore Power BI Desktop
  • Know Your Workspace
  • Import Data
• Learn Data Analysis in Power BI
  • Explore Statistics
  • Group and Bin Data
  • Identify Outliers
  • Apply Clustering
  • Time Series Analysis
• Conclusion

Introduction to Power BI

Power BI is a business analytics tool that Microsoft owns. It is one of the leading business intelligence tools trusted by most IT companies in the market to improve their business growth. It provides advanced analytic tools, reports and visualizations. Power BI is much versatile and platform-independent tool that user can embed on the cloud, mobile and web apps.

What is Data Analysis?

Data analysis is a process of inspecting, transforming, and monitoring to turn raw data into valuable insights. Data Insights helps in making the required decisions for the growth of business and company. To lead a data-driven approach for a business, it is important to analyze the data in deep. Various techniques for Data Analysis in Power BI will be interesting and beneficial for you to learn.

Explore Power BI Desktop

Power BI Desktop is a computer application that contains all the features used for analyzing data. You can download this application from Microsoft’s official website or store.

Know Your Workspace

1. Use this ‘Get Data‘ option to import your data from various sources like Excel, Azure SQL Server, Web and many more.
2. ‘Reports‘ tab shows all the visualizations and reports that are created.
3. ‘Data‘ tab is used to view and modify all the imported data.
4. ‘Models‘ tab is used to create and manage the relationship between multiple tables.
5. These are the number of pages or reports used in the project. Click on the plus icon to add a new page.
6. You can paste visualization, charts and graphs in this area.
7. ‘Filters‘ window contains all the filters that we can apply to our visualization and charts.
8. The ‘Visualizations‘ window contains all the charts that user can use in a report.
9. This is the ‘Fields‘ section that is used to provide data to the visualization chart.
10. This is the ‘Format‘ section to enhance the appearance of the chart.
11. This is the ‘Analytics‘ section to adjust the visualization.
12. ‘Fields‘ window contains all our imported data and tables.

Import Data

To import data, visit ‘Home‘ and press the ‘Get Data‘ option in the menu. Choose your data source and import it. For beginners, simple data with fewer entries will be easier to understand the concept. If you want to follow the steps with us, you can use our sample data. Download our sample data from here.

Learn Data Analysis in Power BI

Power BI provides various tools and charts to analyze data efficiently. We will use some of the basic techniques for data analysis in Power BI to generate valuable insights.

Explore Statistics

1. Visit the ‘Reports‘ tab to start creating visuals and charts.
2. Select or drag any visualization and paste it on the screen. We used a stacked column chart on the left and an Area chart on the right, as shown in the above image.
3. Visit this ‘Fields‘ section to manage your charts or visualization.
4. These are all the properties of a chart, and these properties change depending on the chart you select. You can play with all these properties to learn them better. Let’s discuss some most used ‘Fields’ in a chart.
   • Axis – It is the X-Axis of the area chart. The fields section is currently assigned with Country Codes in the above image, and the same is reflected in the chart on the X-Axis.
   • Legend – This field is used to compare data present in a single field. When some field like Country is assigned in Legend, the chart will show the country various colours to compare them easily.
   • Values – This the Y-Axis of the area chart.
5. Click on the drop-down button present at the end of the values field. Here you can apply some operators on the values field.
6. Power BI will reflect all the changes you made in the fields section on the chart. Here, our graph will change the shape depending on the operator and the resultant values on the Y-Axis.

Play with different visualizations and the fields settings to better learn the functioning of Power BI.

Group And Bin Data

Grouping in Power BI allows merging different categories into a single group, whereas binning allows refining the size and figures used in representing the data. For data analysis in Power BI, grouping and binning plays an important role.

Group Data

In analyzing data, it might be difficult to analyze the various fields of data individually. Suppose if we have data records from various countries and want to group this data based on continents. In our case, we want to create two separate groups, one for Asian countries and the other for the countries that lie outside Asia. Now, we will see the simplest method to create our desired data group.

Use any bar chart and apply some fields on the axis. We used country codes on the x-axis and revenue on the y-axis.

1. Select some of the country bars that belong to Asia and right-click on them.
2. Click on the ‘Group Data‘ option as highlighted in the above image.

1. After grouping, you will see a new group field on the ‘Fields‘ window, as shown in the above image.
2. Drag the newly created group field to your Axis.
3. Also, drag the group field to the legend field. This legend field will change the colours of both groups to compare them easily.
4. After completing the above steps, this group of Asian countries will be shown on the screen as highlighted in the above image.
5. This is the other group that is left by grouping all the Asian countries.

This grouping technique allows comparing multiple groups based on different categories.

Bin Data

Binning data helps refine the data figures. Suppose we have some sales data with the customer ID and the number of product quantities they purchased. We want to compare the initial customers and recent customers with product quantity using a bar graph. This will not be feasible to use a bar graph if the number of entries is huge. We can use binning in this case to divide our entries into different chunks. Remember that binning can only be applied when both Axis of the graph contains the Integer or Number fields.

We used a clustered column chart and applied the fields to it. Customer ID is applied to the ‘Axis‘ and Quantity in the ‘Values‘. You could see that this chart is a bit congested, and imagine the scenario if we had more entries in our data. To make this chart more clear, we will use binning.

1. Click the drop-down button on the ‘Axis’ field.
2. Then click on ‘New Group’ as shown in the above image.

The Groups window will appear on the screen as shown in the above image.

1. Enter the name of the bin that you want to create.
2. If you have integer values on the axis, the group type will default to ‘Bin.‘
3. Here you can choose two different Bin type. We will use ‘Number of Bins‘ to see the exact numbers of the bars on your chart. ‘Size of Bin‘ is used to divide the bars based on the bin size.
4. This is the data field that is used in creating bins.
5. ‘Min value’ is the first value of the entries in a Customer ID data field.
6. ‘Max value’ is the last value from the entries that are present in that field.
7. ‘Bin Count’ is the number of bars that you want to see on your chart.
8. Bin size is automatically calculated by Power BI using the formula Bin Size is equal to (Max value – Min value)/Bin Count.

1. After applying all the changes, you will see a new field created on the ‘Fields‘ section with the name you used.
2. Drag the newly created field into the Axis.

You will now notice that all the bars are now grouped into some bins. All the entries are now divided into an equal number of ‘Bin Size’.

Identify Outliers

Comparing the values of the most and least productive data helps us to identify the outliers. In Power BI, we can easily identify the outliers with just a simple scatter chart.

Select a scatter chart from the Visualizations and apply all the field to the scattered chart.

1. We used Product Name in the Legend, Quantity in the X-Axis, Revenue in Y-Axis and Revenue in the Size. Here Size of the circle depends on the field Revenue that we have used.
2. Now, notice the circles in the scatter chart. The blue circle on the top is the product that brings more revenue.
3. The Red circle on the top right is the product that brings more purchase, and the size of the circles shows the revenue it brings.

Similarly, all the circles show some properties in this scatter chart. With this single scatter chart, we can bring more valuable insights.

Apply Clustering

Clustering is much similar to grouping and binning. In a scatter chart, we can have an infinite number of circles if the numbers of records are huge. So it is better to divide those circles into clusters. Power BI automatically calculate the clusters based on their values and properties they reflect.

Use a scatter chart and apply the fields as shown in the above image.

1. We used Product Name in the details, Quantity in X-Axis, Revenue in Y-Axis and Revenue in Size. We left Legend empty because we will use our cluster field in this area.
2. Click on the ‘More Options‘ at the top of the scatter chart, as shown in the above image.
3. Then click on the ‘Automatically find Clusters‘. This option will not be visible if you have used the wrong fields in your scatter chart.

This cluster window will now pop up on your screen.

1. Enter the name of the Cluster that you want to use.
2. This is the fields name that Power BI will use in creating clusters.
3. Enter the number of clusters that you want to create in your chart. Leave this section empty if you want Power BI to create default clusters for your data automatically.

1. A new field will be added, as shown in the above image.
2. Drag the newly created cluster to the legend field.

Your scatter chart will now be divided into the number of clusters that you want. In our case, the purple colour cluster at the bottom is the product names that bring the least number of sales and revenue. The orange colour cluster brings average revenue but more sales in quantity. The blue colour cluster brings max revenue but an average number of sales.

Time Series Analysis

Time Series Analysis helps visualize the time-based scenario. Till now, we have learned the advantage of the scatter chart. We will now learn to make use of a scatter chart to analyze the data with time.

Before applying the below steps, make sure you have used the right fields in the scatter chart.

1. To use the time series analysis, you only need to add a time or data field in the ‘Play Axis‘ of the scatter chart, as shown in the above image.
2. Tap this play button to start the animation of the scatter chart.

In the above animation, you can see how the values are changing with time. These animations are very helpful to compare the product sales and analyze the overall growth of a company.

Conclusion

Power BI is no doubt an effective tool for analyzing data. This tool can help data analyst to keep an eye on the huge data generated by the company. Through this blog, we are now familiar with the ways for effective data visualization in Power BI. We have learned some basic techniques that you can use to generate valuable data insights.

In this blog, we only learned the basics of Data Analysis in Power BI. If you want to get deep knowledge and want to become a Data Analyst to solve business problems, then our Microsoft Certified Data Analyst Associate [DA-100] Certification course will help you achieve your dreams. It will help you to learn and unlock all the features of Power BI to its full potential.

References

• Introduction to Data Modeling in Power BI
• Introduction to Microsoft Power BI Platform | Everything You Must Know
• Microsoft Certified Data Analyst Associate [DA-100]: Everything You Need To Know
• Microsoft Certified Data Analyst Associate [DA-100] Step By Step Activity Guides (Hands-On Labs)

Next Steps to begin with DA-100 Certification:

In our Microsoft Data Analyst Associate Certification Training Program, we’ll cover 11 Hands-On Labs. If you wish to start your journey towards becoming a Microsoft Certified: Data Analyst Associate, try our FREE CLASS.

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