Day 5 of My Data Analytics Internship: Data Transformation, Background Settings, and Cards in Power BI - T K Sharvesh Blogger

A Step-by-Step Guide to Cleaning Data with Power Query, Customizing Visuals, and Creating Cards

Raw data is rarely perfect. It often contains errors, missing values, inconsistent formatting, and irrelevant columns that can compromise the quality of any analysis. The ability to transform and clean data is one of the most critical skills for any data analyst. Day 5 of my 15-day data analytics internship at Prompt Info Tech was dedicated to mastering data transformation techniques in Microsoft Power BI, along with learning how to customize visual backgrounds and create cards for displaying key metrics. This blog post provides a comprehensive overview of everything I learned, from using Power Query Editor to clean and prepare data to applying background settings and building cards. Whether you are an intern, student, or professional, these skills are essential for creating accurate and visually appealing reports.

The fifth day of the internship, dated June 19, 2026, focused on the practical aspects of data preparation and report customization. We began by exploring Transform Data in Power BI, which opens the Power Query Editor where we can remove columns and rows, replace null values, use first row as a header, apply split functions, change data types, rename column headings, and create conditional columns. We then moved on to background settings, learning how to change background colors and transparency, and how to add background images. The day concluded with a session on cards, which are used to display single important values like total sales or profit. The TO DOs for the day included replacing null values, practicing conditional columns, trying background settings and images, and the assignment was to create cards.

Power BI Data Transformation Background Settings and Cards Guide

Data Transformation in Power BI

Transform Data in Microsoft Power BI is used to clean, modify, and prepare data before creating reports and visualizations. It opens the Power Query Editor, which provides a powerful environment for data transformation. The TO DO for this section was to replace some null values and practice using conditional columns, emphasizing the practical application of these skills. The first step in data transformation is often removing unnecessary columns and rows. This helps streamline the dataset, making it easier to work with and improving performance. Removing columns eliminates irrelevant data, while removing rows can exclude incomplete or erroneous records. These actions are essential for focusing the analysis on the most relevant data.

Replacing null values is another critical task in data preparation. Null values, or missing data, can skew analysis and lead to incorrect conclusions. The process of replacing null values is straightforward. Open Transform Data, select the column, right-click the column, click Replace Values, enter null in the Value To Find field, enter the replacement value, and click OK. This simple step ensures that missing data is handled appropriately, whether by replacing with a default value, the mean, or any other logical replacement. The TO DO to replace some null values provided hands-on practice with this essential technique.

Using the first row as a header is a common requirement when importing data from sources like Excel or CSV files. This function automatically promotes the first row of data to column headings, saving time and ensuring that the data is properly labeled. To do this, open Transform Data, go to the Home or Transform tab, and click Use First Row as Headers. The split function is another powerful tool used to separate data within a column. For example, a column containing Full Name can be split into First Name and Last Name. To use the split function, select the column, go to the Transform tab, click Split Column, choose either By Delimiter or By Number of Characters, select the split option, and click OK. This function is invaluable for breaking down complex data into more usable components.

Changing data types is essential for ensuring that data is interpreted correctly by Power BI. For example, a column that contains numbers should be set to Whole Number or Decimal Number, while dates should be set to Date. To change a data type, select the column, go to the Transform tab, click the Data Type icon, and choose the appropriate type such as Text, Whole Number, Decimal Number, or Date. Renaming column headings improves clarity and makes data easier to understand. This can be done by double-clicking the column heading, typing the new name, and pressing Enter, or by right-clicking the column heading and selecting Rename.

Conditional columns are used to create new columns based on conditions applied to existing data. For example, a conditional column can be created to assign a Pass or Fail status based on marks. To create a conditional column, open Transform Data, go to the Add Column tab, click Conditional Column, give the column name such as Result, set the condition, for example, if Mark is greater than or equal to 50 then Pass, else Fail, and click OK. This powerful feature enables the creation of derived columns that add significant value to the analysis.

Background Settings and Background Images

Background settings in Power BI allow users to customize the appearance of their reports, making them more visually appealing and professional. To access background settings, click Report View, go to Format Page, and open Canvas Settings. From here, you can change the background color and transparency. Transparency settings range from 0 percent, where the background is fully visible, to 100 percent, where the background is hidden. This flexibility allows users to create reports that align with their branding or personal preferences. The TO DO to try one background setting provided an opportunity to experiment with these options.

Background images can also be added to enhance the visual appeal of reports. To add a background image, go to Canvas Background, select Image, and choose an image. Once the image is added, you can set the display options to Fit, Fill, or Normal, depending on how you want the image to be displayed. Removing a background image is equally simple; simply click the Remove option under the Background Image settings. The TO DO to try one background image encouraged hands-on practice with this feature, reinforcing the importance of visual customization in report creation.

Creating Cards in Power BI

Cards are a simple yet powerful visual in Power BI used to display a single value. They are ideal for highlighting key metrics such as total sales, profit, count, or any other important measure. To create a card, open Report View in Microsoft Power BI Desktop and select the Card visual from the Visualizations pane. Drag a field or measure into the card to display a single value. Once the card is created, it can be customized to improve its appearance and readability. Select the card and open the Format Visual pane to customize it. You can change the font size, color, background, title, and transparency. The TO DO for the day, which was to create cards, provided practical experience in building this essential visual.

Card background customization is done by navigating to Visual, then Cards, and selecting Background. The callout value color can be changed by navigating to Visual, then Callout Value. In Power BI, cards can also display values as percentages, adding flexibility to how data is presented. The assignment for the day was to create cards, which reinforced the importance of this visual in displaying key performance indicators and other critical metrics. Cards are widely used in dashboards to provide a quick overview of the most important data points, making them an essential tool for any Power BI user.

Key Takeaways for Aspiring Data Analysts

• Transform Data is Essential: Power Query Editor enables cleaning and preparing data for accurate analysis.
• Remove Unnecessary Data: Removing columns and rows streamlines datasets and improves performance.
• Handle Null Values: Replacing null values ensures data completeness and accuracy.
• Split and Rename Columns: Splitting fields and renaming columns improves data usability and clarity.
• Conditional Columns Add Value: Creating conditional columns enables derived insights based on conditions.
• Customize Backgrounds: Background settings and images enhance report visual appeal and branding.
• Cards Highlight Key Metrics: Cards are ideal for displaying single important values like total sales or profit.

Day 5 of my data analytics internship at Prompt Info Tech provided a deep dive into the essential skills of data transformation and report customization in Power BI. From cleaning data using Power Query Editor to customizing backgrounds and creating cards, every concept was practical and immediately applicable. These skills are the foundation of effective data analysis, as they ensure that the data being analyzed is accurate, organized, and presented in a visually appealing manner. I encourage fellow interns and aspiring analysts to practice these techniques regularly, as they are critical for producing high-quality reports and dashboards. The ability to transform data effectively and create professional visuals is what sets great analysts apart, and this day marked a significant step forward in that journey.