Top 15 Power BI Projects for Beginners, Intermediates, & Advanced Professionals

Power BI has more than 40 million monthly active users due to its robust analytics capabilities and attractive visuals that can help you sift through a mountain of data, understand it thoroughly, and make smart decisions. It is a group of several software services, connections, and apps that collaborate to transform disparate data sources into immersive and interactive visualizations. 

PowerBI projects provide you with the opportunity to convert data sources and create individualized, interactive PowerBI dashboards and reports in real-time. In this blog, we will look at some of these projects for beginners, intermediates, and advanced.

What is Power BI?

Power BI is a combination of apps, connectors, and software services that can help transform unstructured data into visually engaging, logical, and interactive insights.

Your data may be stored in a collection of cloud-based and on-premises hybrid data warehouses as well as in an Excel spreadsheet. Connecting your data sources, identifying what is relevant, and producing your findings are all made simpler with Power BI. You can learn more about Power BI and how it can be used in projects by pursuing a Power BI course.

Three principles serve as the foundation for all other components that make up Power BI. These include:

• Windows desktop software called Power BI Desktop.
• Power BI mobile applications for Windows, iOS, and Android.
• Software-as-a-Service (SaaS) is a term used to describe the Power BI service.

Beginner-Level Power BI Projects

Given below are some Power BI sample projects for beginners. You can learn about business intelligence applications and have a rewarding career as a data scientist by completing these straightforward Power BI tasks.

Also Read: Power BI Interview Questions

1. Study of Customer Churn

One of the simplest and most well-liked Power BI sample projects is the customer churn study project. Customer churn analysis reveals regional customers’ product sales and earnings.  Analytical users can use it to analyze regional business expansion across geographies to divide profits among clients and get insightful information. Upon employing the correct visuals and data structures, they can receive a lot of information. 

The project takes into account local financial inflows and recurring product-specific consumer attrition. You can use the Kaggle customer segmentation dataset for this easy-to-understand exercise. You can also use combo charts, cards, bar charts, tables, or line charts for the analytical overview page, and column charts, bubble charts, point maps, tables, etc. for the customer segmentation page.

2. Examination of Product Sales Data

For many reasons, businesses keep thorough records of their sales. Yet, if there is an excessive amount of data, it becomes challenging to keep track of everything. Analyze the sales data of a company to determine profit by product, sales, and other major aspects that might have an impact on a company’s performance.

Microsoft’s sample dataset is available for use. Before visualizing and exporting the filtered data as dashboards, connect the data set to Power BI Desktop. This step will eliminate irrelevant data. The dashboard may include pie charts, bar charts, and doughnut charts, among other Power BI visualization styles.

3. Study of Healthcare Sales Among Animals

This project is for people working in the animal healthcare industry to effectively monitor the sales of goods used to treat small animal species. Companies may quickly compare product sales between cities with the highest and lowest rankings. The project could produce a comprehensive report on sales by therapeutic group and the sales trends for any given time frame.

You can consult the Kaggle-hosted pharmaceutical sales dataset. To create a data visualization in Power BI, use the following features:

• Tables for displaying therapeutic group-based sales
• Column charts for monthly sales trends
• Bar charts for top and bottom-ranking cities
• Treemaps and cards for smart narratives

For user-friendly Power BI resources for beginners, you can also visit the Microsoft Power BI Forum.

4. Inventory Management Dashboard

In this project, you will create an interactive dashboard that will help show inventory levels, reordering triggers, and turnover rates. The aim is to enable a company to reorder timely and maintain inventory. 

Use Kaggle’s inventory dataset with product details, stock quantities, sales history, and reorder levels as the base of this project. You can create columns for reordering suggestions, calculate stock turnover rates, identify low-stock products, and forecast reorder quantities.

5. Website Traffic Analysis

It is a popular Power BI project that aims to analyze website traffic to understand traffic sources, user behavior, and trending content. These insights will help a company create prompt content according to the target audience and strategize investments in successful traffic sources. 

Utilize the dataset with details like user sessions, engagement, page views, and referral sources. You can use it to identify the best referral sources, analyze page views by URL, calculate bounce rate, and derive average session duration.

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Intermediate-Level Power BI Projects

Here are some Power BI projects if you are already familiar with the Power BI data visualization tool. With the help of these projects, you can improve your data analytics abilities as a data scientist.

6. Study of Global Health Expenditures

The Global Health Spending Analysis is among the best Power BI project ideas. Using PyCaret, this project implements clustering analysis in Power BI. Data elements with comparable features are grouped using the clustering technique. These categorizations support data analysis, pattern detection, and study of datasets. Data clustering supports the discovery of underlying data structures.

Use the WHO Global Health Expenditure database’s most recent health expenditure dataset in this situation. The dataset includes health spending as a proportion of national GDP for more than 200 countries from 2000 to 2018. 

The K-Means clustering algorithm is another option for the clustering analysis. When you have loaded the dataset in Power BI Desktop and trained your clustering model in Power BI, visualize the cluster labels in Power BI Dashboard to obtain insights. You can select visualization chart kinds for the summary page dashboard, such as filled maps, bar charts, etc. while using point maps for deeper visualization.

7. Analysis of Loan Applications

The idea behind this project is to analyze loan application data to identify abstract themes, which will subsequently determine how particular subjects (loan kinds) affect the default rate. The topic model works using Latent Dirichlet Allocation (LDA) in Power BI. The goal of topic modeling is to automatically identify the abstract “themes” of a group of documents.

You can use the Kiva dataset on GitHub for this project, which contains loan information for 6,818 accepted potential borrowers. Once the topic weights are added to the original dataset in Power BI, you may load the dataset into the Power BI desktop.

Also Read: Power BI Developer Salary

8. Movie Sales Forecasting

The goal of this project is to create an interactive visual experience from a dataset that tracks movie sales over time. You can utilize the 2006–2016 IMDb dataset, which is accessible on both IMDb and Kaggle. The dataset comprises different variables like Budget, Gross, Genre, and Scores as well as a list of films with an IMDb score of 6 or above.

This would show the selected set’s metrics like Average Score and Gross Collections. Plotting a histogram of the score variable with year and genre as slicers can show the frequency distribution of the IMDb scores.

9. E-Commerce Conversion Funnel

Work on this project to create a funnel analysis dashboard to track user interactions on e-commerce websites and optimize the conversion process. Use an e-commerce dataset from Kaggle to begin. You will find data like landing page visits, user actions, timestamps, completed purchases, etc. 

You can track user sessions, calculate conversion rates, categorize user actions, and identify points of drop-offs and factors behind abandoned carts. Use bar graphs or funnel charts for visualization and suggest strategies to optimize conversion rates accordingly.

10. Social Media Engagement Dashboard

The objective of this project is to create a dashboard that tracks and visualizes social media engagement metrics across platforms. Use Kaggle’s social media engagement dataset that includes key metrics, such as comments, likes, shares, and followers. 

Use Power Query and DAX to aggregate data from various platforms and calculate engagement rates. You can integrate external APIs for real-time data. You can also use Power BI’s visualization elements like bar charts, heat maps, and line charts to identify the popular content type, top engagement periods, and effectiveness of different social media strategies.

Advanced-Level Power BI Projects

Here is a list of some of the top Power BI developer projects for experts who are looking for challenging datasets to work on.

11. Investigation of Covid-19 Insights

One of the most well-liked Power BI projects is the COVID-19 insights analysis. It intends to provide a comprehensive summary of the key elements of the COVID-19 epidemic, the current condition, and complete country-level assessments.

A dashboard that provides useful data on cases (active, died, or recovered), death rates, and recovery rates by country and time frame can be made. A full examination of each report page and a management summary of the most significant KPIs may also be included in the dashboard.

For this project concept, consult the COVID-19 dataset available on Kaggle. As for the Power BI data visualization tools, you can use the following:

• Doughnut charts for category-wise case analysis.
• Bar charts for the overview page.
• Point maps, line charts, and column charts.

12. Energy Trade Analysis Worldwide

The analysis of global energy trade is among the most original concepts for Power BI initiatives. This project’s concept covers a range of issues related to the production and exchange of energy worldwide. It discusses a variety of subjects, including the development of wind energy and the use of energy to compare the economies of different countries.

Use the Kaggle dataset of global energy statistics for this project. The dashboard could include the following data: 

• Production
• Trade
• Consumption of primary and secondary energy
• Conventional and non-conventional energy sources
• Renewable energy sources

Power BI visualization types include bar charts, flow maps, cards for the overview page, ribbon charts, treemaps, and bar charts for energy generation. These can all be used for the dashboard.

13. Analysis of Life Expectancy Data

This study evaluates life expectancy statistics based on a variety of factors, such as vaccines, mortality, finances, social factors, and other health-related issues. It will be simpler for a nation to pinpoint the determinant causing a lower life expectancy figure. In this way, a nation can advise on how to increase the life expectancy of its population effectively.

Utilize the WHO’s life expectancy dataset on Kaggle to analyze life expectancy data. The overview page can employ Power BI visualization styles, such as gauge charts, pie charts, line charts, and point maps. The country-specific data can be shown using tornado charts, doughnut charts, treemaps, etc.

14. Global Supply Chain Optimization

This Power BI project aims to analyze the global supply chain process to identify inefficiencies and optimize the process. Choose a dataset with information on suppliers, lead times, inventory levels across supply chains, and transportation routes. Use Power Query to consolidate and preprocess data from multiple sources. Use DAX to calculate several supply chain metrics. Visualize geographic distribution with the help of different charts and point maps. These visualizations will help identify bottlenecks in the supply chain, regions with high shipping costs, and more.

15. Natural Language Processing (NLP) Insights

You can create a project to integrate natural language processing techniques into Power BI and extract insights from textual data sources, such as customer feedback, surveys, and reviews. 

Use datasets containing customer feedback, reviews, and associated metadata. Clean the textual data and use DAX to perform sentiment analysis and visualization. It will help you get information like frequently mentioned keywords, sentiment distribution across products, and areas of improvement.

Why Power BI?

Power BI is one of the best business intelligence (BI) tools in the world for the following reasons:

• Linking to Data Sources: Power BI supports a variety of data sources, making it simpler for users to match data sets from different sources and use them to produce visually appealing reports. It has over 70 connectors that can pull data from Dropbox, OneDrive, CSV, Excel, Azure Data Warehouse, Google Analytics, and other sources.
• High Performance: The Power BI pivot data modeling engine is a columnar database that greatly boosts performance metrics by reducing original data sets from 1 GB to 200 MB.
• Custom Visualization: It offers a wide range of pre-made data visualizations that can be incorporated into interactive reports. The reports can also benefit from custom visualizations created and developed by in-house developers in the Power BI community.
• R Integration for Advanced Analytics: The computer language R offers thousands of packages with a focus on data mining and visualization. Data scientists utilize the programming language R for machine learning, statistical calculation, and complex data modeling. We may import data models developed in R directly into Power BI dashboards and turn them into visualizations.
• Integration with Cortana: Power BI also works with Microsoft’s digital assistant, Cortana. It allows users to use their voice to access dashboard content and ask questions about data. Even by only speaking or typing their searches, users of mobile devices might generate several views.

Conclusion

Through this blog, we’ve learned how important it is to use Power BI in a variety of projects across several sectors to gain practical insights into different sets of information. These real-time Power BI projects will help novice and seasoned data scientists improve their skill sets and understand the integration of business information into data science. 

Out of the ones mentioned in this blog, which Power BI project did you like the best? Share with us in the comments section below. If you are interested in pursuing a career in Power BI, refer to this detailed blog on how to become a Power BI developer and get to know about career scope, salary, and tips.

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