Every fresher with a Power BI certificate has the same line on their resume: "Proficient in Power BI." A recruiter reading fifty resumes a day cannot tell who means it. A project can. When you attach a dashboard that answers a real business question, built on messy data and explained in plain language, you stop being one more name on the list and become someone who has already done the job once. This guide gives you eight projects that do exactly that, each with a clear business question, a dataset to start from, the Power BI skills it proves, and what to put on the final page so a hiring manager gets it in thirty seconds.
What Hiring Managers Look For in a Power BI Project
A project gets noticed for the thinking behind it, not for how colourful it is. Before you pick one, keep these four checks in mind:
- A real question. "Why did sales fall in the south region?" beats "Sales dashboard".
- Messy data you cleaned. Show the Power Query steps: removed duplicates, fixed data types, handled blanks.
- A proper data model. A date table, a fact table and dimension tables joined in a star schema signal that you know how Power BI actually works.
- A finding in plain words. End with two or three sentences a manager could act on.
Power BI Desktop is free to download and runs on Windows, so you can build all eight projects without spending anything.
The 8 Projects at a Glance
| # | Project | Business question | Main skills proved | Level |
| 1 | Retail sales dashboard | What sold, where and when? | KPIs, time intelligence, slicers | Beginner |
| 2 | HR attrition dashboard | Who is leaving and why? | Calculated measures, segmentation | Beginner |
| 3 | UPI payments tracker | How is digital payment volume growing? | Trends, growth rates, forecasting | Intermediate |
| 4 | Nifty 50 portfolio dashboard | How is a portfolio performing against the index? | Returns, moving averages, comparison | Intermediate |
| 5 | Food delivery operations dashboard | Where are delays and cancellations coming from? | Operational KPIs, drill-through | Intermediate |
| 6 | Loan risk dashboard | Which borrowers are likely to default? | Risk segmentation, conditional formatting | Intermediate |
| 7 | State-wise public data dashboard | How do Indian states compare on a key indicator? | Maps, public data cleaning | Beginner to intermediate |
| 8 | Marketing funnel dashboard | Which channel brings customers at the lowest cost? | Funnel analysis, cost metrics | Advanced |
1. Retail Sales Dashboard
The question: Which products, regions and months drive revenue, and where is it leaking?
This is the classic first project, and it stays popular because every company has sales data. Use a retail or superstore-style dataset from Kaggle, or build one in Excel with order date, product, category, region, quantity, discount and amount in . Make it feel Indian by looking at the festive quarter around Diwali against the rest of the year.
What to build: A one-page overview with total sales, profit, orders and average order value in ; a monthly trend line; sales by region and category; and a slicer for year. Add a year-over-year measure:
Total Sales = SUM(Orders[Amount])
Sales LY = CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
YoY Growth % = DIVIDE([Total Sales] - [Sales LY], [Sales LY])
Skills it proves: Star schema, a proper date table, basic DAX and time intelligence.
What to put on the page: One headline finding, such as "Furniture earns the highest sales but the lowest margin because of heavy discounts."
2. HR Attrition Dashboard
The question: Which employees are leaving, and what do they have in common?
HR analytics is a steady hiring area because every large company tracks attrition. Public HR datasets (the IBM HR attrition sample on Kaggle is widely used) include age, department, tenure, salary band, overtime and whether the employee left.
What to build: Overall attrition rate, attrition by department, by tenure band and by salary band, plus a drill-through page for one department.
Attrition Rate = DIVIDE(
COUNTROWS(FILTER(Employees, Employees[Attrition] = "Yes")),
COUNTROWS(Employees)
)
Skills it proves: Calculated columns for banding (such as 0 to 2 years, 3 to 5 years), percentage measures and the habit of segmenting before concluding.
What to put on the page: A finding that points to action, like "Attrition is highest among employees with under two years of tenure who work regular overtime."
3. UPI Digital Payments Tracker
The question: How fast is digital payment volume growing, and which months or banks stand out?
India's UPI story is one of the most discussed data stories in the country, and interviewers love candidates who work on live, familiar topics. NPCI publishes monthly UPI statistics, including transaction volume and value, on its website. Download the monthly series, clean it in Power Query and build a time-series view.
What to build: Monthly volume and value trends, month-on-month and year-on-year growth, average transaction value (value divided by volume) and a simple forecast using Power BI's built-in forecasting on the line chart.
Skills it proves: Handling a real published dataset, building growth measures and using the analytics pane for trend lines and forecasts.
What to put on the page: A clear note on the source and date range, and one insight, such as how average ticket size changes as volume grows. Check NPCI's site for the current format of its tables before you start, since layouts change.
4. Nifty 50 Portfolio Dashboard
The question: How does a sample portfolio perform against the Nifty 50 index?
Finance recruiters like candidates who can work with market data. Pull historical daily prices for five to ten Nifty 50 stocks and the index from NSE or from a free source such as Yahoo Finance, and load them into Power BI.
What to build: Daily and cumulative returns, a 50-day and 200-day moving average, a comparison of each stock against the index, and a volatility view. Keep a clear note that this is an educational exercise and not investment advice.
Skills it proves: Working with large date-indexed tables, rolling calculations in DAX and comparison visuals. It also shows domain interest if you are targeting finance, investment banking or equity research roles.
What to put on the page: A one-line takeaway such as which stock added the most return per unit of risk over the period, with the method stated.
5. Food Delivery Operations Dashboard
The question: Where do delays, cancellations and poor ratings come from?
Quick commerce and food delivery are among the most visible industries in Indian cities, which makes this an easy project to explain in an interview. Datasets with delivery time, distance, city, order value, rating and cancellation status are available on Kaggle.
What to build: Average delivery time by city and hour of day, cancellation rate by reason, rating versus delivery time, and a drill-through from city to individual orders.
Skills it proves: Operational KPIs, drill-through pages, tooltips and the ability to turn a messy time field into usable hour buckets.
What to put on the page: An operations insight, such as "Evening peak hours show the longest delivery times and the highest cancellation rate."
6. Loan Risk Dashboard
The question: Which borrower segments carry the highest risk of default?
Banks, NBFCs and fintech companies hire analysts for credit and risk work, and this project signals that you understand how lending data behaves. Use a public lending or credit dataset with loan amount, interest rate, income band, employment length, purpose and loan status.
What to build: Default rate by income band, loan purpose and employment length; a heat-style table with conditional formatting; and a measure comparing each segment with the overall default rate.
Skills it proves: Segmentation, percentages versus counts (a segment with few loans can look risky purely by chance), and conditional formatting that guides the reader's eye.
What to put on the page: The riskiest and the safest segment, with the sample size beside each so the numbers are not over-read.
7. State-Wise Public Data Dashboard
The question: How do Indian states compare on an indicator that matters, such as literacy, rainfall, crop production or road accidents?
India's open government data portal (data.gov.in) and RBI's data publications offer state-level datasets. They are rarely clean, which is exactly why this project impresses: it proves you can work with real, imperfect data.
What to build: A filled map by state, a ranked bar chart, a trend over years and a small table of the top and bottom five states. Pick one indicator and stay with it rather than mixing many.
Skills it proves: Power Query cleaning (inconsistent state names, merged headers, footnotes), map visuals and careful sourcing.
What to put on the page: The source, the year of the data and one observation, such as which state improved most over five years.
8. Marketing Funnel Dashboard
The question: Which channel brings customers at the lowest cost, and where do prospects drop off?
Marketing analytics is one of the most employable areas for Power BI. If you do not have real campaign data, create a realistic dataset with spend, impressions, clicks, leads and conversions across channels such as Google Ads, Meta, email and organic. State clearly that the data is simulated.
What to build: A funnel from impressions to clicks to leads to customers, with these measures:
CTR % = DIVIDE(SUM(Ads[Clicks]), SUM(Ads[Impressions]))
Cost per Lead = DIVIDE(SUM(Ads[Spend]), SUM(Ads[Leads]))
Cost per Customer = DIVIDE(SUM(Ads[Spend]), SUM(Ads[Customers]))
Add a channel comparison table, a monthly spend versus conversions chart and a parameter that lets the viewer change a budget and see the projected effect.
Skills it proves: Funnel analysis, ratio measures, what-if parameters and commercial thinking.
What to put on the page: A recommendation, such as moving budget from the channel with the highest cost per customer to the one with the lowest.
How to Present Your Projects So They Get Read
A great dashboard can still be ignored if nobody can find or understand it. These habits take an hour and change how your work is received.
Write a short case note for every project. Four lines are enough: the problem, the data source, what you did and what you found. Put it on the first page of the report and in your portfolio.
Show the data model. A screenshot of your star schema tells a hiring manager more about your skills than the charts do.
Keep each page to one message. Five to seven visuals with a clear title that states the finding ("Evening orders are slowest") work better than a crowded page.
Publish carefully. Power BI's free account lets you publish reports to the web, but a report published this way is public to anyone with the link. Use only open or simulated data, never anything from a current or former employer. You can also share a short screen recording or screenshots, and keep the .pbix file on GitHub.
Tailor the order to the job. Applying to a bank? Lead with the loan risk and Nifty projects. Applying to an e-commerce company? Lead with retail and food delivery. Put your two strongest, most relevant projects first.
Which Projects to Pick Based on Your Goal
| Your target | Start with | Then add |
| Fresher, any analyst role | Retail sales, HR attrition | State-wise public data |
| Finance or banking | Nifty 50 portfolio, loan risk | UPI tracker |
| Marketing or growth | Marketing funnel, retail sales | Food delivery operations |
| Operations or supply chain | Food delivery operations | Retail sales |
| Moving from Excel to BI | Retail sales | HR attrition |
Three polished projects are better than eight rushed ones. Build two from this list, finish them properly with a case note, a clean model and one clear finding, and then add the third.
Should You Also Get Certified?
Microsoft offers the Power BI Data Analyst Associate certification, taken through the PL-300 exam. It covers preparing data, modelling, visualising and analysing, and deploying and maintaining reports, so it lines up well with the projects above. A certificate shows you know the tool, and a project shows you can use it. Having both is the strongest combination, but if you must choose where to spend your time, spend it on projects first.
FAQs
The retail sales dashboard is the best starting point because the data is easy to understand and it covers the basics: a data model, a date table, KPIs and time intelligence. The HR attrition dashboard is a close second and adds segmentation.
Two or three well-finished projects are better than a long list. Pick the ones closest to the role you are applying for, and give each a one-line outcome, such as the business question and the tool features used.
Kaggle has retail, HR, lending and delivery datasets. India's open government data portal (data.gov.in) and RBI's data publications have state-level and economic data. NSE and Yahoo Finance provide historical market prices, and NPCI publishes monthly UPI statistics. Always check the licence and note the source on your report.
You can build a basic dashboard with drag-and-drop visuals, but DAX is what turns a pretty report into an analytical one. Learn a handful of measures first: SUM, DIVIDE, CALCULATE, FILTER and a time-intelligence function such as SAMEPERIODLASTYEAR. That is enough for all eight projects here.
Power BI Desktop is free to download and use on Windows. Sharing reports with colleagues through the Power BI service needs a paid licence at most companies, but you do not need one to build and showcase portfolio projects.
