Excel Projects for a Data Analyst Portfolio: 6 Ideas With Datasets

Last updated: 13 October 2026 · By the Asuraa Team

Quick answer: Excel portfolio projects should prove the work analysts do daily: cleaning exports, pivot summaries, lookups, scenarios and a one-sheet dashboard. Six options: RFM segmentation on UCI Online Retail (541,909 transactions), student results on UCI Student Performance (649 students), a World Happiness comparison, bike rental patterns on UCI Bike Sharing (2011-2012), an SIP return calculator on AMFI NAV history, and a Walmart sales forecast with FORECAST.ETS.

Key takeaways

• Excel portfolio projects should show cleaning, pivot summaries, lookups, scenario modelling and a one-sheet dashboard on real data.
• The UCI Online Retail dataset has 541,909 transactions from a UK online gift retailer between 1 December 2010 and 9 December 2011.
• The UCI Student Performance dataset covers 649 students from two Portuguese schools, with grades G1 to G3 on a 0-20 scale.
• AMFI's NAV history page lets users retrieve historical NAVs for a scheme over a period, which supports an SIP and XIRR calculator project.
• Using Power Query and named tables makes an Excel project refreshable, which matters more to reviewers than chart styling.

Which Excel projects belong in a data analyst portfolio?

The Excel projects that belong in a data analyst portfolio show the tasks analysts do daily in spreadsheets: cleaning raw exports, building pivot-table summaries, writing lookups, modelling simple scenarios and presenting a dashboard on one sheet. The six projects below each use a real public dataset and focus on a different Excel skill.

Excel remains part of most analyst job descriptions alongside SQL. The Excel skills for jobs guide covers which functions to learn first; these projects are where you prove them.

What does each Excel project involve, step by step?

Each project lists a goal, steps, what it proves and an example resume bullet. Bracketed parts are placeholders for your own results.

1. Customer RFM segmentation

Goal: group customers by recency, frequency and monetary value so marketing can target them. Dataset: UCI Online Retail, 541,909 transactions from a UK-based online gift retailer between 1 December 2010 and 9 December 2011, with invoice, product, quantity, price, customer and country fields.

1. Load the file through Power Query, since it is too large to work with comfortably as a plain sheet.
2. Remove cancelled invoices and rows without a customer ID; add a line-total column.
3. Group by customer to get last purchase date, number of invoices and total spend.
4. Score each measure 1 to 5 with PERCENTRANK or quintile cut-offs.
5. Pivot customers by segment and chart revenue share.

Shows: handling a large export and a standard marketing framework. Example resume bullet (example only): "Built an RFM model in Excel and Power Query on 541,909 retail transactions; showed the top [n] segments produced [x]% of revenue."

2. Student results analysis

Goal: a results dashboard a school head could use to spot students at risk. Dataset: UCI Student Performance, 649 secondary students from two Portuguese schools with demographic, social and school attributes and grades G1, G2 and G3 on a 0-20 scale.

1. Add grade bands with IFS or a lookup table.
2. Use COUNTIFS and AVERAGEIFS to compare results by study time, absences and family support.
3. Flag students whose G2 dropped sharply from G1.
4. Build a one-sheet summary with conditional formatting.

Shows: conditional aggregation and a practical early-warning rule. Example resume bullet (example only): "Analysed 649 student records in Excel with COUNTIFS and AVERAGEIFS; flagged [n] at-risk students whose grades fell by [x] points."

3. Country wellbeing comparison

Goal: compare India with other countries on happiness score and its components. Dataset: World Happiness Report on Kaggle, which scores countries on factors such as economic production and social support.

1. Combine the yearly sheets into one table with a year column.
2. Use XLOOKUP (or INDEX-MATCH) to pull India and five comparison countries.
3. Apply colour scales to show strengths and gaps by factor.
4. Plot score against GDP factor in a scatter chart and label outliers.

Shows: lookups, combining files and clear comparison visuals. Example resume bullet (example only): "Combined [n] years of World Happiness data in Excel; benchmarked India against [x] countries with lookups and a factor heatmap."

4. Bike rental demand patterns

Goal: explain how weather, season and hour of day affect rentals. Dataset: UCI Bike Sharing, hourly and daily rental counts for 2011 and 2012 with weather and seasonal information.

1. Load the daily file first, then the hourly file.
2. Pivot average rentals by season, weekday and weather situation.
3. Build an hour-by-weekday heatmap with conditional formatting.
4. Add slicers and pivot charts to make a small interactive dashboard.

Shows: pivot tables, slicers and pattern-finding. Example resume bullet (example only): "Built an Excel pivot dashboard on 2011-2012 bike-share data showing rentals fell [x]% in poor weather and peaked at [hour] on weekdays."

5. Mutual fund return calculator

Goal: compare returns of two or three mutual fund schemes for lump-sum and monthly SIP investing. Data: AMFI's NAV history page, which lets you retrieve historical NAVs for a scheme over a period and compare a scheme's NAV on two dates.

1. Retrieve historical NAVs for your chosen schemes and paste them into a table.
2. Calculate point-to-point and annualised returns with date functions.
3. Simulate a monthly SIP and compute its XIRR.
4. Add a data table showing outcomes for different SIP amounts and start dates.

Shows: financial functions and scenario modelling, useful for fintech and banking analyst roles. This is a spreadsheet exercise, not investment advice. Example resume bullet (example only): "Built an Excel SIP calculator on AMFI NAV history using XIRR and data tables to compare [n] schemes over [x] years."

6. Store sales forecast

Goal: forecast weekly sales for a few stores and measure forecast error. Dataset: Walmart Dataset on Kaggle, store sales data intended for sales prediction.

1. Pick three stores and arrange weekly sales in date order.
2. Hold back the last few months as a test period.
3. Forecast with FORECAST.ETS and compare with a simple moving average.
4. Calculate mean absolute percentage error for both methods.
5. Chart actual versus forecast with confidence bounds.

Shows: forecasting with honest evaluation. Example resume bullet (example only): "Forecast weekly sales for [n] stores with Excel FORECAST.ETS; reduced error versus a moving average from [x]% to [y]% MAPE."

How do you share Excel projects in a portfolio?

Share each workbook on GitHub or a cloud drive with view access, plus screenshots and a short note explaining the question and method. Reviewers rarely download files, so the README or a one-page PDF should carry the story.

1. Keep raw data, working sheets and the dashboard on separate tabs.
2. Use named tables, not loose ranges.
3. Add a "Notes" tab listing cleaning steps and formulas used.
4. Export the dashboard sheet as an image or PDF for the README.

The guide on how to build a portfolio for jobs shows how to host files, and Excel interview questions for data analysts covers the functions interviewers ask about.

What do most guides on Excel projects get wrong?

Most guides present Excel projects as chart exercises, but analysts are judged on whether the numbers are right and repeatable. A dashboard built on manually edited cells breaks the next time data arrives. Using Power Query for cleaning and named tables for formulas shows you can build something a team can refresh.

Many guides also pick tiny, tidy datasets. The Online Retail file, with cancellations and missing customer IDs, is a better test because it forces real cleaning decisions.

Finally, guides often skip validation. Cross-check one pivot total against a SUMIFS formula and mention it in your notes; that habit is exactly what managers look for.

How do Excel projects fit with SQL and Power BI work?

Excel projects show spreadsheet fluency, but most analyst portfolios pair them with one SQL project and one dashboard-tool project. The projects and portfolio guide for India explains the mix, and the data analyst roadmap for India shows when to move from Excel to SQL. Once your bullets are written, run them through the Asuraa resume reviewer against a real analyst listing.

Related guides

• Projects and Portfolio Guide for Freshers: Data, Software and AI
• Power BI Projects for a Portfolio: 6 Dashboards With Public Data
• Machine Learning Projects for Final Year Students: 10 Dataset Ideas
• Python Projects for Freshers: 8 Resume-Ready Ideas by Level

FAQ

What Excel projects should a data analyst fresher build?

Build projects that show cleaning, pivots, lookups and a dashboard on real data. A customer RFM segmentation on the UCI Online Retail file, a pivot dashboard on UCI Bike Sharing data and an SIP return calculator using AMFI NAV history together cover Power Query, pivot tables, XLOOKUP, XIRR and scenario tables.

Is Excel still enough for a data analyst job in India?

Excel is necessary but rarely enough on its own. Most analyst job descriptions also ask for SQL, and many mention Power BI or Tableau and some Python. Use Excel projects to prove spreadsheet fluency, then add one SQL project and one dashboard project so your portfolio matches the full role.

Can Excel handle large datasets like Online Retail?

Yes, with Power Query. The UCI Online Retail dataset has 541,909 transactions, which is heavy as a plain sheet but manageable when loaded and cleaned in Power Query and summarised with pivots. This also shows reviewers a repeatable workflow, since the query can be refreshed when new data arrives.

How do I show an Excel project on GitHub?

Upload the workbook with raw data, working sheets and the dashboard on separate tabs, plus a README with screenshots, the business question, cleaning steps and key formulas. Many reviewers will not download a file, so a dashboard image or one-page PDF in the README is what they actually see.

Which Excel functions should a portfolio project show?

Show XLOOKUP or INDEX-MATCH, SUMIFS, COUNTIFS and AVERAGEIFS, IF or IFS logic, date functions, pivot tables with slicers, and Power Query for cleaning. For finance-flavoured roles add XIRR and data tables; for planning roles add FORECAST.ETS with an error measure such as MAPE.

How do I write an Excel project as a resume bullet?

Name the dataset size, the Excel features used and the finding. An example only: Built an RFM model in Excel and Power Query on 541,909 retail transactions; showed the top [n] segments produced [x]% of revenue. Replace the brackets with your real results and link the workbook or screenshots.

Final thoughts

Two or three Excel projects built with Power Query, named tables and a clear one-sheet dashboard prove you can do an analyst's daily work. Pair them with SQL and you have a credible fresher portfolio. When you are ready, look for analyst roles on Asuraa jobs.