Microsoft Power BI: Getting Started
A guide for your first data transformation and visualization project.
Data visualization is a powerful tool that transforms complex and often large datasets into visually compelling and easy-to-understand representations. It bridges the gap between raw data and actionable insights by presenting information in a graphical format that is intuitive, engaging, and accessible. Effective data visualization helps individuals quickly grasp patterns, trends, and relationships within the data but also aids in making informed decisions and identifying opportunities for improvement. With advanced technologies and tools, data visualization has become indispensable in various fields, including business, science, healthcare, education, and more.
Why Microsoft Power BI?
Microsoft Power BI is an excellent choice for beginners entering the world of data visualization and business analytics. Its intuitive and user-friendly interface makes it remarkably accessible, even for those with limited technical expertise. With a wide range of pre-built templates, drag-and-drop functionality, and a variety of visualization options, beginners can quickly transform raw data into engaging and informative reports and dashboards.
Power BI’s seamless integration with familiar tools like Excel further simplifies the learning curve for beginners. Its self-service capabilities allow users to connect to different data sources, clean and transform data with ease, and create dynamic visuals without requiring extensive coding knowledge. The interactive nature of Power BI’s visuals allows beginners to explore data intuitively, facilitating a deeper understanding of patterns and insights.
Furthermore, Power BI’s active user community and extensive documentation provide ample resources for beginners to learn, troubleshoot, and expand their skill set. The availability of online tutorials, forums, and sample datasets ensures that newcomers can quickly build confidence and proficiency in creating impactful visualizations. Overall, Microsoft Power BI’s combination of user-friendly features, integration with familiar tools, and abundant learning resources makes it a top choice for beginners venturing into the world of data visualization and analytics.
Download and Installation
Now that you are ready for your first hands-on project in Power BI, let's download the software and explore its features. For our project, we will be using “Power BI Desktop” as it is free software and a great tool for exploration. I will keep this guide more of a picture-based one and give you some room to explore yourself, rather than just explaining every tini-tiny bit.
System Requirements:
NOTE: These are general requirements and may vary based on the complexity of your data and the size of the reports and dashboards you’re working with.
Operating System:
- Windows 10 (64-bit) or higher
Processor:
- 1.6 GHz or faster, dual-core processor
- 2.0 GHz or faster recommended
Memory (RAM):
- 4 GB RAM or more
- 8 GB RAM or more is recommended for better performance with large datasets
Storage:
- 2 GB of available hard disk space
Display:
- Monitor with at least 1024x768 resolution
- High-DPI (100% scaling) or better recommended for better visuals
Graphics:
- DirectX 10 compatible graphics card for hardware acceleration
- DirectX 11 compatible graphics card or better recommended for improved performance
Internet:
- An active internet connection is required for some features, such as data refresh and online services
Installation:
STEP 1: Open Microsoft Store on your PC/Laptop and search for “Power BI Desktop”.
STEP 2: Once you find the “Power BI Desktop” in the search results, click on it and click on “Get”.
STEP 3: After that, the download will start automatically. And as soon as the downloading is done, the program will install automatically.
STEP 4: Once download and install in over, open “Start Menu” in your Windows system and search for “Power BI”.
STEP 5: Once you locate the program, click on the icon once to start the program.
STEP 6: Skip any sign-in for now. You should have a clean and new instance of Power BI desktop running on your system.
Importing Data
There are numerous ways of importing data into Power BI and it supports a number of file types, from .xlsx to .csv and many more.
You can click on the “Get data” button on the Home tab to explore various ways and file types to import data.
Connect to source data:
Load data:
For this practice project, we will use sample data provided in the program itself.
- To get to the sample data, go to the Help tab and click on the “Examples” button and then click on “Sample dataset”:
2. After that click on “Load sample data”:
3. After that, a Navigator window will open. In that select the “financials” dataset and then click on the “Load button”.
After following the above steps the sample data should be successfully loaded in the program.
Preview data
Now that you have successfully loaded the data, you can preview it by clicking on the “Table view” tab on the left pane of the program.
After that, a preview as shown in the below image, should be visible.
Note that the total rows count in the data is by default available at the bottom left corner.
Take your time to look around this dataset, try to understand what it is about and try to search if there are any mistakes in the data.
Data Transformation
Data transformation is an essential step in any data visualization (DV) project within the context of Power BI, as it enables raw, disparate data to be refined and shaped into a coherent and actionable form. Through transformations, such as cleaning, filtering, merging, and adding calculated columns, data inconsistencies are rectified, irrelevant information is removed, and the foundation for meaningful insights is established. Effective data transformation ensures that the visualizations accurately reflect the underlying information, leading to more accurate analyses, insights, and ultimately, better decision-making.
Power BI has a dedicated sub-program called “Power Query Editor” which does all the heavy lifting for you in the data transformation process.
To open the Power Query Editor, click on the Home tab and then click on the “Transform data” button.
Once you have done the previous steps, a new Power Query Editor window should open and be visible to you.
Now that the Power Query Editor window is open, let’s perform various transformation operations on data, which will make it more sensible. Let’s start with changing data types.
Changing Data Types:
As you can see in the image below, The dataType of the column “Units Sold” is decimal. And rows 1 and 12 in fact have data in decimal value. But if you really think about it, the no. of units is a kind of data that logically cannot be a decimal value but should be a whole number.
So let’s change the dataType of this column from decimal to whole number.
For that click on the dataType symbol on the top left corner of the column, to see the options of different dataType. Now, select “Whole Number”.
Since conversion to whole numbers will remove all the decimal points and their successive value, you will be asked to replace values in the current table or add another column. Since the decimal dataType is indeed a mistake in data and there is no point in preserving it, we will replace the values in the current table itself.
Similarly, if you notice, then the dataTypes of all the columns containing prices are in decimal format. Power BI has a dedicated dataType called “Fixed Decimals” for handling data representing money or similar amount. So you can change the dataTypes of all such columns to “Fixed Decimal”.
I will leave this part up to you to explore yourself.
Merging Columns:
Power BI offers powerful different ways to merge different columns, let’s try them out.
As you might have noticed, we have a “Date” column and three other columns where the date is split into day, month and year respectively. We might feel this is unnecessary and would want to see all of this data merged into a single column.
For that, Select the three columns together, click on the Transform Tab, and select on “Merge Columns” button.
After that, you will be asked to enter the symbol which separates the merged values. You can either select None or enter your custom symbol and in the last text box, you can enter the name of the merged column. In our case, we named it “Date merged.”
After that, you will find the columns merged into a single column called “Date Merged”.
Removing Columns:
Now that we have merged, the columns into one column called “Date Merged”, you might feel that the original “Date” column is now irrelevant and would want to remove it.
For that Select the Date column by simply clicking on the column name once, then in the Home Tab, click on the “Remove Columns” button.
After the above, the “Date” column should disappear.
Add Columns:
After removing the Date column, you might realise that it was an important column as it was in the proper date dataType and you cannot perform date-related operations efficiently on the “Date Merged” column. So you want it back. But How?
Luckily in Power BI, you can always undo any step by removing the performed step from the “Applied Steps” dialogue box in the right pane.
But what if that’s not an option right now?
Well, you can make use of the “Date Merged” column itself and bring the original “Date” column back.
For that, Select the “Date Merged” column, go to Add Column tab and under “Date” option, select “Parse”, as shown in image below.
After that, a new column “Parse” will appear beside the “Date Merged” column containing the date in date dataType. You can always rename the column back to “Date” by double-clicking the column name.
Split Columns:
Now that you have recovered the “Date” column, you might also want to split the “Date Merged” column back to the three original columns.
For that, Select the “Date Merged” column, go to the Transform tab and under the “Split Column” button, select “By Delimiter”.
After doing that a dialogue box will open.
Don’t make many changes here, as the program automatically detects the delimiter, just select “None” under Quote Character and click OK.
After that, you will see the “Date Merged” column split back into three columns. Of course, you will need to rename them.
Exploring options to remove missing, null and duplicate values:
Although our dataset doesn’t have any errors or missing values, Power BI gives you options to automatically remove or repair such data points, as shown in the image below.
Data Visualization
Now that we have transformed data as per our needs, click on the “Close & Apply” button to save the changes and reload the data with the changes.
Now, to make visualizations, you will need to navigate to “Report View”.
For that, you can simply click on the “Report View” button just above the “Table View” button in the left pane.
Once you have entered the “Report View”, you can start making visualization.
For a start, select any visualization you want. Here we have selected the Stacked column chart.
As soon as you select the type of chart, in our case, a Stacked column chart a pseudo chart appears on the screen with its various controllable parameters like X-axis, Y-axis, etc under the Visualization pane.
Drag and drop the Segment data on the X-axis and Sales data on Y-axis.
You will automatically be presented with a column chart that shows Total sales per Segment. Cool Right?
After you have done the above, you have successfully created a visualization out of raw data. Congratulations!
Now I will leave you alone to explore and play around with different visualization and their various parameters, because “self-learned is best learned”.
As an exercise, you can try to achieve a dashboard similar to the one displayed in the below image.
I hope this basic guide would have helped you to give a kick-start on your Data Engineering and Analytics journey. But don’t just limit yourself to here, there are many awesome free as well as paid courses available online for mastering Power BI.
After you feel that you have got quite a grasp on the program, you can appear for the Power BI certification exam by Microsoft. The certification is of high value in the industries and can really make your Resume shine among the others.
It is a skill in high demand in a lot of industries nowadays and the job pays well as well.
Thank you so much for reading.
