𝐃𝐚𝐲 𝟑- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

Whenever I load data from Excel into Power BI and clean it in Power Query Editor, 𝐈 𝐮𝐬𝐞 𝐜𝐚𝐫𝐝𝐬 𝐚𝐧𝐝 𝐬𝐥𝐢𝐜𝐞𝐫𝐬 𝐭𝐨 𝐜𝐫𝐨𝐬𝐬-𝐜𝐡𝐞𝐜𝐤 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐦𝐲 𝐄𝐱𝐜𝐞𝐥 𝐬𝐡𝐞𝐞𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐜𝐫𝐞𝐚𝐭𝐞 𝐯𝐢𝐬𝐮𝐚𝐥𝐬.

This step ensures that the number of rows and columns, as well as the data within them, match accurately. Verifying this early helps prevent any discrepancies or issues that could arise later in the analysis process.

𝐅𝐨𝐫 𝐞𝐠., if I'm working with sales data, I'll compare total revenue figures between Excel and Power BI to ensure they align. Additionally, I might use slicers to filter data by specific criteria, such as date ranges or product categories, and verify that the filtered results match what I expect from the original Excel data.

𝐃𝐚𝐲 𝟒- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

My company requires me to use the same font, color, and organizational background in each Power BI report. I don't want to manually set the size and font for each report, so I'll use JSON in Power BI to apply these settings automatically.

𝐎𝐧𝐜𝐞 𝐭𝐡𝐞 𝐉𝐒𝐎𝐍 𝐭𝐡𝐞𝐦𝐞 𝐢𝐬 𝐜𝐫𝐞𝐚𝐭𝐞𝐝, 𝐢𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐚𝐩𝐩𝐥𝐢𝐞𝐝 𝐭𝐨 𝐚𝐧𝐲 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐫𝐞𝐩𝐨𝐫𝐭, 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐬𝐚𝐯𝐢𝐧𝐠 𝐭𝐢𝐦𝐞.

1. Open Notepad or Visual Studio Code

2. Modify the JSON content to match your company’s branding guidelines. For example, change the dataColors, background, and fontFamily.
{
"name": "Company Theme",
"dataColors": ["#005A9E", "#F28E2B", "#76B7B2", "#59A14F", "#EDC948", "#AF7AA1"],
"background": "#FFFFFF",
"foreground": "#000000",
"tableAccent": "#005A9E",
"title": {
"*": {
"fontFamily": "Arial",
},

3. Save the file with a .𝐣𝐬𝐨𝐧 extension, for example, company-theme.json.

4. Open Power BI & Go to the 'Home' tab--> ‘Switch Theme'-->‘Browse for themes'--> Choose you JSON file-->'Open' and reports are ready with same themes. 😄

𝐃𝐚𝐲 𝟓- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

When you build a data model in Power BI for your project, there are few things that you need to do:

1. 𝐓𝐮𝐫𝐧 𝐨𝐟𝐟 𝐀𝐮𝐭𝐨 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐭𝐢𝐦𝐞 otherwise Power BI creates a hidden data table for every data column in your model

2. 𝐓𝐮𝐫𝐧 𝐨𝐟𝐟 𝐁𝐢𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠. A lot of people see it as a way to bring the filter from one Dimension table to the other through the fact table. However, Bidirectional Filtering slows down your model by a lot. Also, if you have multiple fact tables, it could lead to ambiguity, it means you could see wrong values in your report.

3. Go for a star schema because that is what Power BI is optimized for.

Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made.

When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended.

This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

𝐃𝐚𝐲 𝟔- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

When creating a dashboard in Power BI, one common challenge is selecting colors, especially when you're not sure of the exact color codes you want to use.

To address this, I recommend visiting website- 𝐡𝐭𝐭𝐩𝐬://𝐜𝐨𝐥𝐨𝐫𝐡𝐮𝐧𝐭.𝐜𝐨/ where you can find a variety of color palettes.

These palettes often include corporate colors that can be suitable for your report. Simply choose a color, copy its code, and paste it into Power BI's font options. You can also adjust the transparency to your preference.

This simple process allows you to quickly find the perfect colors for your report. 🎨

𝐃𝐚𝐲 𝟕- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

I have two tables: Orders and Cookies.

Orders:
1. Product
2. Units Sold

Cookie Types:
1. Cookie Type
2. Revenue per Cookie

I have joined these two tables in the model view through "Product" from the Orders table and "Cookie Type" from the Cookies Type table in one direction. My client wants to see the total amount.

𝐖𝐡𝐚𝐭 𝐃𝐀𝐗 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐬𝐡𝐨𝐮𝐥𝐝 𝐈 𝐮𝐬𝐞?

Let's see. I have tapped on "New Column" in the Orders table and written this:
𝐀𝐦𝐨𝐮𝐧𝐭 = 𝐎𝐫𝐝𝐞𝐫𝐬[𝐔𝐧𝐢𝐭𝐬 𝐒𝐨𝐥𝐝] * '𝐂𝐨𝐨𝐤𝐢𝐞 𝐓𝐲𝐩𝐞𝐬'[𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐩𝐞𝐫 𝐜𝐨𝐨𝐤𝐢𝐞]

No, I can't write this DAX function. Power BI will not allow me to write this.

Instead, I can write:
𝐀𝐦𝐨𝐮𝐧𝐭 = 𝐎𝐫𝐝𝐞𝐫𝐬[𝐔𝐧𝐢𝐭𝐬 𝐒𝐨𝐥𝐝] * 𝐑𝐄𝐋𝐀𝐓𝐄𝐃('𝐂𝐨𝐨𝐤𝐢𝐞 𝐓𝐲𝐩𝐞𝐬'[𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐏𝐞𝐫 𝐂𝐨𝐨𝐤𝐢𝐞])

When you use 𝐑𝐄𝐋𝐀𝐓𝐄𝐃, you are telling Power BI to look up the corresponding value from the related table based on the existing relationship(relationship which we made in model view).

𝐓𝐡𝐞 𝐑𝐄𝐋𝐀𝐓𝐄𝐃 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐚𝐥𝐥𝐨𝐰𝐬 𝐲𝐨𝐮 𝐭𝐨 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐚𝐛𝐥𝐞𝐬 𝐭𝐨 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐢𝐧𝐯𝐨𝐥𝐯𝐞 𝐜𝐨𝐥𝐮𝐦𝐧𝐬 𝐟𝐫𝐨𝐦 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐭𝐚𝐛𝐥𝐞𝐬.

Even if you have used a bidirectional relationship, you still need to use the 𝐑𝐄𝐋𝐀𝐓𝐄𝐃 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 when creating calculated columns that involve data from related tables.

𝐃𝐚𝐲 𝟖- 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐑𝐞𝐚𝐥 𝐓𝐢𝐦𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐞𝐫𝐢𝐞𝐬 📊

My client gave me TSV files—yes, you heard right, TSV files and not CSV files. TSV stands for 𝐓𝐚𝐛-𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐝 𝐕𝐚𝐥𝐮𝐞𝐬, a type of file where data is stored in a plain text format and each field is separated by a tab character. I need to load these TSV files into Power BI to create the dashboard.

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧:

Rename File: Rename your .tsv file to .csv.
Now,
Launch Power BI Desktop-->"Home" tab--> "Get Data"-->Choose "Text/CSV"--->Select that file--->"Load" tab

I'm mentioning this detail because many users face issues when trying to load TSV files into Power BI. By default, Power BI looks for CSV files, which can cause confusion and prevent TSV files from appearing in the file selection dialog.

Here are the detailed answers to each of the Power BI interview questions that have been asked at Infosys, TCS & Wipro:

1. How can you ensure that Power BI recognizes a specific column as a date column if it doesn't do so automatically?
- You can change the data type of the column in Power Query Editor or in the Data View. Select the column, then use the data type dropdown to select "Date" or "Date/Time."

2. Describe the process Power BI uses to handle large datasets exceeding the in-memory capacity.
- Power BI can handle large datasets by using techniques such as aggregations, incremental refresh, and DirectQuery mode. DirectQuery allows Power BI to query data directly from the source without loading it into memory, while aggregations can summarize data at a higher level to reduce the amount of data processed.

3. Can you explain the role of the Power BI service in the overall Power BI architecture?
- The Power BI service (PowerBI.com) is a cloud-based service that provides various features like sharing, collaboration, and dashboarding. It allows users to publish, share, and manage reports, create dashboards, and collaborate with others in their organization. It also supports data refresh, scheduled refreshes, and gateways to connect to on-premises data sources.

4. What are the key components of data modeling in Power BI?
- The key components of data modeling in Power BI include tables, relationships, measures, calculated columns, and hierarchies. Data modeling involves defining how data from different sources relates to each other and creating a model that supports analysis and reporting.

5. What is the purpose of the VertiPaq engine in Power BI?
- The VertiPaq engine is an in-memory columnar database engine used by Power BI to compress and store data efficiently. It allows for fast query performance by storing data in a highly compressed format and retrieving only the necessary columns for a given query.

6. How do you create a stacked area chart in Power BI?
- To create a stacked area chart, go to the Report View, select the data fields you want to visualize, and then choose the "Stacked Area Chart" option from the visualizations pane.

7. What is the difference between a clustered bar chart and a stacked bar chart?
- A clustered bar chart displays bars for each category grouped side by side, allowing for comparison between categories. A stacked bar chart, on the other hand, stacks the bars on top of each other, showing the total value while also displaying the contribution of each category to the total.

8. Explain the concept of role-based access control (RBAC) in Power BI.
- Role-based access control (RBAC) in Power BI allows administrators to define roles with specific permissions and assign users to these roles. This ensures that users only have access to the data and reports they are authorized to view, enhancing security and data governance.

9. What is a calculated column in Power BI, and how is it different from a measure?
- A calculated column is a column that is created using a DAX formula to add new data to a table in the data model. It is calculated row by row. A measure, however, is a DAX formula used to perform calculations on aggregated data, and its result can change depending on the context of the report or visualization.

10. How can you create and apply a custom data category in Power BI?
- You can create and apply a custom data category by selecting the column in the Data View or Power Query Editor, and then choosing the appropriate data category from the "Modeling" tab in the ribbon. Custom data categories can include geographic data, URLs, and other types.

11. What are the different methods to optimize data load performance in Power BI?
- Methods to optimize data load performance include using DirectQuery mode for real-time queries, reducing the number of columns and rows loaded into memory, using aggregations to summarize data, optimizing data transformations in Power Query, and leveraging incremental refresh for large datasets.

12. Can you outline the Power BI ecosystem and its major components?
- The Power BI ecosystem consists of Power BI Desktop, Power BI Service, Power BI Mobile, Power BI Report Server, and Power BI Embedded. Power BI Desktop is used for creating reports and dashboards, the Power BI Service is a cloud-based platform for sharing and collaboration, Power BI Mobile allows viewing reports on mobile devices, Power BI Report Server is for on-premises report deployment, and Power BI Embedded is for integrating Power BI reports into custom applications.

13. What is the difference between a dataflow and a dataset in Power BI?
- A dataflow is a collection of data transformation processes in Power BI that are reusable and can be shared across multiple reports and datasets. A dataset, on the other hand, is a single source of data created from one or more data sources that is used in Power BI reports and dashboards.

14. How does the DirectQuery mode work in Power BI, and when would you use it?
- DirectQuery mode allows Power BI to directly query the underlying data source in real-time without importing data into memory. This mode is useful when working with very large datasets, ensuring data is always up-to-date, and minimizing the amount of data loaded into memory.

15. How do you create a waterfall chart in Power BI?
- To create a waterfall chart, go to the Report View, select the data fields you want to visualize, and then choose the "Waterfall Chart" option from the visualizations pane. This type of chart shows the cumulative effect of sequential positive and negative values.

16. What are the advantages and disadvantages of using a scatter plot in Power BI?
- Advantages: Scatter plots can show the relationship between two numerical variables, highlight clusters and outliers, and reveal trends and correlations. Disadvantages: They can become cluttered with too many data points, making it hard to interpret, and may require additional context to understand the data fully.

17. Explain the concept of incremental refresh in Power BI.
- Incremental refresh allows Power BI to refresh only the data that has changed or been added since the last refresh, rather than reloading the entire dataset. This reduces the time and resources required for data refreshes, making it suitable for large datasets with frequent updates.

18. What is the purpose of the "Group By" feature in Power BI, and how is it used?
- The "Group By" feature in Power BI allows users to group rows in a table based on one or more columns and perform aggregations (e.g., sum, average) on the grouped data. It is used in the Power Query Editor to simplify and summarize data before loading it into the data model.

19. How can you handle time zone conversions in Power BI?
- Time zone conversions can be handled by using DAX functions to adjust date and time values based on the desired time zone. You can use functions like TIMEZONEOFFSET to calculate the difference between time zones and adjust the datetime values accordingly.

20. What techniques can be used to reduce the file size of a Power BI report?
- Techniques to reduce file size include removing unnecessary columns and rows, using aggregations to summarize data, optimizing data transformations in Power Query, disabling or removing unused visuals, and reducing the number of visuals on a single report page.

21. Describe the different layers involved in Power BI architecture.
- The Power BI architecture consists of the following layers: Data Source Layer (connects to various data sources), Data Transformation Layer (uses Power Query to clean and transform data), Data Modeling Layer (defines relationships, calculated columns, and measures), Visualization Layer (creates reports and dashboards), and Service Layer (manages sharing, collaboration, and data refresh).

In recent 2 years Power BI market share has increased upto 60 % & became No.1 BI Tool defeating Tableau ❤️❤️

In many Big MNCs report migration Projects are going on like:

Tableau to Power BI ✅
Qlik to Power BI ✅
Looker to Power BI ✅

Still no one could not finish Excel 😅

Start Learning 🖌

Practical interview question for an entry-level data analyst role in #Power_BI: along with answers !

Question: Data Modeling Case: You have sales data and customer data in separate tables. How would you model this data in Power BI to analyze customer purchase behavior?

👉 Load the Data: Import the sales data and customer data tables into Power BI.

👉 Establish Relationships: Identify the CustomerID as the common key between the two tables. In the "Model" view, create a relationship by connecting the CustomerID column from the Sales Data table to the CustomerID column in the Customer Data table.

👉 Data Structure:

Sales Data Table: Contains columns like SaleID, CustomerID, ProductID, SaleDate, and Amount.

Customer Data Table: Contains columns like CustomerID, CustomerName, Age, Gender, and Location.

👉 Create Visualizations:

Total Sales by Customer: A bar chart showing the total amount spent by each customer.

Sales Over Time: A line chart displaying sales trends over time for each customer.

Customer Demographics: Pie charts or bar charts illustrating sales distribution by customer age, gender, and location.

👉 Utilize DAX for Advanced Analysis: Create measures using DAX (Data Analysis Expressions) to calculate total sales and sales by specific customer attributes for deeper insights.

By following these steps, you can effectively model your data in Power BI to gain meaningful insights into customer purchase behavior.

Life of a data engineer
👇👇
https://t.iss.one/sql_engineer/67

In Tableau, Level of Detail (LOD) expressions allow you to control the granularity of aggregations. Power BI has similar functionalities through the use of DAX (Data Analysis Expressions). While Power BI doesn't have direct LOD expressions, you can achieve the same results using DAX functions. Here are some common scenarios and their DAX equivalents:

1. Fixed LOD Expressions:
In Tableau, a fixed LOD expression computes values at a specific granularity, independent of the visualization's granularity.

Tableau:

   { FIXED [Dimension1], [Dimension2]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), ALLEXCEPT('Table', 'Table'[Dimension1], 'Table'[Dimension2]))
   

2. Include LOD Expressions:
An include LOD expression adds a specific dimension to the granularity of the existing view.

Tableau:

   { INCLUDE [Dimension]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), ALL('Table'[Dimension]))
   

3. Exclude LOD Expressions:
An exclude LOD expression removes a specific dimension from the granularity of the existing view.

Tableau:

   { EXCLUDE [Dimension]: SUM([Measure]) }
   

Power BI (DAX):

   CALCULATE(SUM('Table'[Measure]), REMOVEFILTERS('Table'[Dimension]))
   

4. Row-level Calculations:
To perform calculations at the row level and then aggregate the result, you can use the SUMX function in DAX.

Tableau:

   SUM([Measure1] + [Measure2])
   

Power BI (DAX):

   SUMX('Table', 'Table'[Measure1] + 'Table'[Measure2])
   

These DAX functions allow you to achieve similar results as Tableau's LOD expressions by giving you control over the context and granularity of calculations.

Here are some essential visualizations you can use in your projects

Bar Chart:
Example: Comparing the number of apples, bananas, and oranges sold at a fruit stand. Reason: Bar charts are good for comparing the amounts of different items side by side.

Column Chart:
Example: Showing how many hours a student studied each day for a week. Reason: Column charts help you see how values change over specific periods, like days or months.

Line Chart:
Example: Tracking the temperature at noon each day for a month. Reason: Line charts are perfect for showing trends over time, like how the temperature goes up and down.

Pie Chart:
Example: Showing the percentage of time a student spends on different activities in a day: studying, playing, eating, and sleeping. Reason: Pie charts are great for showing parts of a whole, making it easy to see which activity takes up the most time.

Area Chart:
Example: Visualizing how much money a student saves each month, adding up over a year. Reason: Area charts emphasize the total amount growing over time, like a savings account.

Scatter Chart:
Example: Plotting the relationship between the number of hours studied and the grades received on tests. Reason: Scatter charts show how two things are related, helping to see if studying more leads to better grades.

Map (Bubble Map):
Example: Showing where all the students in a class live on a map, with bigger bubbles for more students. Reason: Maps are good for showing data that is spread out over different places, making it easy to see where most students live.

Table:
Example: Listing the names, ages, and favorite subjects of students in a class. Reason: Tables are useful for showing detailed information in a clear, organized way.

Card:
Example: Highlighting the total number of books read by a student in a year(KPI). Reason: Cards are used to show important single numbers that stand out.

Gauge:
Example: Showing how close a student is to their goal of reading 50 books in a year. Reason: Gauges help visualize progress towards a goal, like a speedometer shows how fast a car is going.

Tree Map:
Example: Displaying the amount of time spent on different subjects in a month, with bigger boxes for more time. Reason: Tree maps show how different parts of a whole are divided, making it clear which subjects take the most time.

Funnel Chart:
Example: Analyzing the steps a student takes to complete a project: research, writing, editing, and finalizing. Reason: Funnel charts show a process and where things might slow down or get stuck.

Waterfall Chart:
Example: Breaking down the total score in a video game by showing points earned and lost in different levels. Reason: Waterfall charts help see how an overall value is affected by positive and negative changes.

I have curated the best interview resources to crack Power BI Interviews 👇👇
https://topmate.io/analyst/866125

Hope you'll like it

Like this post if you need more resources like this 👍❤️

𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐧𝐠 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 💡

𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 👇
You are a data analyst for a global e-commerce company. You need to analyze the performance of your marketing campaigns across different regions and identify which campaigns have the highest return on investment (ROI). Additionally, you want to see how customer acquisition costs (CAC) vary by region and campaign.

𝑸𝒖𝒆𝒔𝒕𝒊𝒐𝒏 👇
How would you use Power BI to create a comprehensive report on marketing campaign performance and ROI analysis?

𝑨𝒏𝒔𝒘𝒆𝒓:
For this we are provided with three datasets:

𝐂𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬: CampaignID, CampaignName, Region, StartDate, EndDate, Budget
𝐒𝐚𝐥𝐞𝐬: SaleID, CampaignID, SaleAmount, SaleDate
𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬: ExpenseID, CampaignID, ExpenseAmount, ExpenseDate

▶ 𝑺𝒕𝒆𝒑 1: Analyze the dataset thoroughly and perform some data cleaning and transformation steps 📈

▶ 𝑺𝒕𝒆𝒑 2: Create Measures that are required in accordance with scenario given.

Total Sales = SUM(Sales[SaleAmount])
Total Expenses = SUM(Expenses[ExpenseAmount])
ROI = DIVIDE([Total Sales] - [Total Expenses], [Total Expenses])
Customer Acquisition Cost (CAC): CAC = DIVIDE([Total Expenses], DISTINCTCOUNT(Sales[SaleID]))

▶ 𝑺𝒕𝒆𝒑 3: Use appropriate filters and visuals according to your requirements. You may use clustered column chart for CAC by region, line chart for sales and expense trends, can add slicers for region, campaign name, and date range, etc.

▶ 𝑺𝒕𝒆𝒑 4: Analyze the project for some informative insights and trends.

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Complete Power BI Topics for Data Analysts 👇👇

1. Introduction to Power BI
- Overview and architecture
- Installation and setup

2. Loading and Transforming Data
- Connecting to various data sources
- Data loading techniques
- Data cleaning and transformation using Power Query

3. Data Modeling
- Creating relationships between tables
- DAX (Data Analysis Expressions) basics
- Calculated columns and measures

4. Data Visualization
- Building reports and dashboards
- Visualization best practices
- Custom visuals and formatting options

5. Advanced DAX
- Time intelligence functions
- Advanced DAX functions and scenarios
- Row context vs. filter context

6. Power BI Service
- Publishing and sharing reports
- Power BI workspaces and apps
- Power BI mobile app

7. Power BI Integration
- Integrating Power BI with other Microsoft tools (Excel, SharePoint, Teams)
- Embedding Power BI reports in websites and applications

8. Power BI Security
- Row-level security
- Data source permissions
- Power BI service security features

9. Power BI Governance
- Monitoring and managing usage
- Best practices for deployment
- Version control and deployment pipelines

10. Advanced Visualizations
- Drillthrough and bookmarks
- Hierarchies and custom visuals
- Geo-spatial visualizations

11. Power BI Tips and Tricks
- Productivity shortcuts
- Data exploration techniques
- Troubleshooting common issues

12. Power BI and AI Integration
- AI-powered features in Power BI
- Azure Machine Learning integration
- Advanced analytics in Power BI

13. Power BI Report Server
- On-premises deployment
- Managing and securing on-premises reports
- Power BI Report Server vs. Power BI Service

14. Real-world Use Cases
- Case studies and examples
- Industry-specific applications
- Practical scenarios and solutions

You can refer this Power BI Resources to learn more

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Hope it helps :)

Useful tips to select Chart Type

👉 If you have categorical data,then either use a bar chart if you have more than 5 categories or a pie chart for less than 5 categories.

👉 If you have nominal data, use bar charts ; histograms if your data is discrete,; line/ area charts if it is continuous.

👉 If you want to show the relationship between values in your dataset, use a scatter plot, bubble chart, or line charts.

👉 If you want to compare values, use a pie chart — for relative comparison — or bar charts — for precise comparison.

👉 If you want to compare volumes, use an area chart or a bubble chart.

👉 If you want to show trends and patterns in your data, use a line chart, bar chart, or scatter plot.