Hi friends! 🤗
I recently worked on a Power BI project using a Coca-Cola dataset, and I wanted to share my experience. The dataset included the following columns:
✅ Retailer name and retailer ID
✅ Invoice date
✅ Regions (Midwest, Northeast, South, Southwest, and West)
✅ States and cities
✅ Beverage brands (Coca-Cola, Dasani, Diet Coke, Fanta, Powerade, and Sprite)
✅ Price per unit, units sold, total sales, operating profit, and operating margin
Before creating my Visualizations I took it to transform data (a tab in powerbi to clear out unnecessary and repeated information)
How I Structured My Power BI Visualization
1️⃣ Organizing the Data
I broke the dataset into four quarters (Q1–Q4) and further divided each quarter by month (January, February, etc.).
I categorized the data by regions to allow for clearer comparisons between Midwest, Northeast, South, Southwest, and West.
The retailer IDs were also included for more detailed tracking.
I added a dropdown feature for the beverage brands so that I could manage space on the dashboard. Instead of displaying all the beverage names at once, users could click the dropdown to see them.
2️⃣ Creating the Visualizations
📊 Histogram: Used to display total sales per month across different retailers. This helped in understanding which months had the highest and lowest sales.
🍩 Donut Chart: Illustrated the sum of units sold per beverage brand to provide insights into which beverage performed best.
📈 Stacked Bar Chart: Showed total sales by retailer, helping to compare revenue generation across different sellers.
This project took hours to complete, but I loved every bit of the process!
I have attached a PDF file of the Power BI dashboard for reference. However, note that the dropdown function won’t be visible in the picture.
I was scrolling on X and I came across an interview question for beginners, and I thought to share it. Although I have written a post on the types of joins, if you want to learn in-depth about the different types of joins we have, you can check out my previous post.
I also includef
- - What is a JOIN in SQL? Be able to explain the different types of joins. This is a question that is seen to be asked by recruiters as a screener question just to make sure they want to pass you along
Answer:
A JOIN in SQL is used to combine rows from two or more tables based on a related column. It allows us to retrieve data that is stored across multiple tables by specifying the relationships between them.
There are different types of JOINs, each serving a specific purpose. Below are the common ones:
1. INNER JOIN
This returns only the records that have matching values in both tables.
Example: We have two tables:
Customers (CustomerID, Name, City)
Orders (OrderID, CustomerID, Product)
To get all customers who have placed an order:
SELECT Customers.CustomerID, Customers.Name, Orders.OrderID, Orders.Product
FROM Customers
INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID;
2. LEFT JOIN (or LEFT OUTER JOIN)
This returns all records from the left table and the matched records from the right table. If there’s no match, NULL is returned for the right table’s columns.
Example: To get all customers, including those who haven’t placed any orders:
SELECT Customers.CustomerID, Customers.Name, Orders.OrderID, Orders.Product
FROM Customers
LEFT JOIN Orders ON Customers.CustomerID = Orders.CustomerID;
3. RIGHT JOIN (or RIGHT OUTER JOIN)
This returns all records from the right table and the matched records from the left table. If there’s no match, NULL is returned for the left table’s columns.
Example: To get all orders, including those that do not have matching customers:
SELECT Customers.CustomerID, Customers.Name, Orders.OrderID, Orders.Product
FROM Customers
RIGHT JOIN Orders ON Customers.CustomerID = Orders.CustomerID;
4. FULL JOIN (or FULL OUTER JOIN)
This returns all records when there is a match in either the left or right table. If there is no match, NULL is returned for missing values in either table.
Example: To get all customers and orders, whether they match or not:
SELECT Customers.CustomerID, Customers.Name, Orders.OrderID, Orders.Product
FROM Customers
FULL JOIN Orders ON Customers.CustomerID = Orders.CustomerID;
5. CROSS JOIN
This returns the Cartesian product of both tables, meaning each row from the first table is combined with every row from the second table.
Example:
SELECT Customers.Name, Orders.Product
FROM Customers
CROSS JOIN Orders;
Understanding JOINs is crucial in SQL, as they allow for efficient data retrieval from multiple tables. Each type of join serves different use cases, and mastering them is essential for working with relational databases.
Conclusion
So that's all I have for this week! As you know, I completed a boot camp training on data analysis and worked on my project using Microsoft Excel and SQL. However, I couldn’t use Power BI at the time because it wasn't running on my laptop. Since getting another laptop, I wasn't able to transfer my project files from the previous one, so I’m starting over.
I’ll be sharing my progress as I work through each dataset again, step by step. Learning data analysis has been both amazing and challenging, but I’m grateful for the concepts I’ve grasped so far. Beyond the boot camp, I’ve also been intentional about self-skilling—learning from online resources and others in the field rather than just relying on what I was taught during the bootcamp.
At the end of the day, continuous learning is key. Let’s keep pushing forward!
