Excel Project Ideas: Dashboards & Automation 2026

For most data analyst jobs, Excel is the first technical skill that the employer tests. Before running SQL scripts, creating visualizations in Python or Power BI, you will be required to work with a disorganized dataset, create pivot tables, construct charts, and interpret the results that you have achieved. And that is the reason why having a good excel portfolio could help you validate your skills more in the interviews. 

This guide brings together 15 Excel projects arranged across three learning tiers. Data analysis assignments will be your starting point, followed by assignments on creating dashboards, and you’ll conclude with automation through Power Query and VBA. Each project will involve a dataset that’s practical in nature, excel skills that you will learn, and interview questions that you will be able to answer.

If you are searching for Excel project ideas to practice on, or Excel projects for data analysis or Excel projects for your resume, then you can find some ideas here.

Why Excel Projects Still Decide Analyst Interviews

Being proficient at Excel is still one of the very first criteria that HR uses to evaluate analytical thinking skills. What the hiring managers don’t need is for you to know all the formulas. What they do need is for you to be able to sort out dirty data, analyze it, and convey the results in an understandable manner.

This is why Excel projects for resume matter. A project that is completed proves much more than just the ability to carry out some functions. It proves that you have the capability to handle datasets, organize information, select appropriate visualization techniques, and analyze the data. This carries much more weight than writing “Excel” in your skills.

To help you understand the progression easily, all projects within this guide have been assigned the basic Excel competencies they cover. You will find out if you are learning about Pivot Tables, XLOOKUP, conditional formatting, Power Query, dashboards, or VBA and which type of interview questions each project can prepare you for.

Preparing to become a data analyst? This Data Analyst Roadmap can help you map out your learning journey. 

Tier 1: Analysis Projects (1-5)

Good dashboards require good analysis. For all your Excel projects for practice, this is where you should start. Before even thinking about making any charts and automations, you should be able to work comfortably with dirty data, to detect patterns in it, and to understand what they mean. These Excel exercises for data analysis are all about the everyday job of an analyst; dataset cleaning, creation of Pivot Tables, trend comparison, and conclusions that can be used for decision-making.

Most of these datasets are publicly available on platforms like Kaggle, making them easy to recreate and expand. If you’re just getting started with exploratory data analysis, Scaler’s free EDA & Data Visualisation course is a great companion before you begin.

1. Sales Data Analysis

Sales data seems very sorted until you start working with the spreadsheet. It is usually filled with duplicate orders, different date formats, lack of product classification, canceled orders, so do not rush into creating your Pivot tables. First clean up the data and then analyze it for products, regions, and time periods to find out what really worked.

2. HR Attrition Analysis

Attrition analysis is less about calculating percentages and more about asking better questions. Are employees leaving one department more often than another? Does experience level matter? Is there a relationship between salary bands and turnover? Use Pivot Tables and charts to explore the data, but be careful not to confuse correlation with causation. A strong analyst knows where the data stops and assumptions begin.

3. Survey Results Analysis

Survey responses are almost never ready for analysis. Cleaning up inconsistencies, handling missing values, and categorizing similar answers will take up more of your time than creating the graphs themselves, which is precisely what makes this assignment worth doing. Once you have organized the data, distill hundreds of answers into one single-page report containing only actionable information.

4. Marketing Campaign Performance Comparison

Even the campaign that gets the most clicks may not be the best one. Evaluate several campaigns based on the following metrics: click-through rate, conversion rate, cost per acquisition, and return on investment to determine which one was the most effective. The recommendation matters more than the spreadsheet behind it.

5. Personal Finance Analyzer

Sometimes the best dataset is your own. Import several months’ worth of income and expenditure data, sort transactions into categories, and analyze your budget against reality to find trends that aren’t immediately apparent. When you’re done, you’ll not only have created a spreadsheet that is helpful in your daily life but will also have learned how to think like an analyst in the process.

Learn the EDA mindset in Scaler’s free EDA & Data Visualisation course.

Tier 2: Dashboard Projects (6-10)

A spreadsheet is far more useful if somebody can understand it within a matter of seconds. This is the aim of using dashboards. Instead of scanning thousands of lines, users will be able to filter their information, track their KPIs, and get answers to all the business questions from one single sheet. The following Excel dashboard projects are created in order to make these types of presentations.

When working on these projects, be just as focused on the layout and charts as you are on the formulas. Good dashboards don’t bombard users with numbers but rather make it clear which information should be prioritized.

6. Interactive Sales Dashboard

Imagine giving your workbook to a sales manager who needs answers within one minute. They should not have to access the underlying data. Create a dashboard using Pivot Charts, slicers, and KPIs that allows the user to filter based on regions, products, and time period by just a few mouse clicks. When a person can make sense of sales performance without any assistance from you on your spreadsheet, then you’ve created an effective dashboard.

7. Finance KPI Dashboard

A finance dashboard works effectively where there is no noise. It does not have to show all possible indicators but should concentrate on those few that affect decision-making processes the income, profit margins, expenses, cash flow, and variance budget. A good design with similar colours and clear KPI cards would beat a dashboard cluttered with various graphs.

8. Project Management Tracker

Project trackers are often updated daily, so simplicity matters. Design a dashboard which will track task status, due dates, percentages of completion, and resource utilization, without the need for users to go through various sheets. The aim is to help someone spot delays instantly without looking for them.

9. Healthcare Metrics Dashboard

The healthcare industry is characterized by rapidly changing data, thus making dashboards a suitable means for analyzing trends through time. Employ patient admission, discharge rates, bed occupancy rates, and other key metrics to develop a report that will be easily comprehensible during meetings. Do not sacrifice clarity for beautification.

10. E-commerce Performance Dashboard

The e-commerce dashboard incorporates sales, orders, consumer behavior, and product performance. It is not necessary to stuff all available space with visual information; one should figure out which KPIs need to be presented with priority. A good dashboard helps the reader to understand where to look first, guiding the eye from the primary KPI to related trends.

Also read: Chart Styling in Excel

Tier 3: Automation Projects (11-15)

Manually doing things will not be scalable. If you have been importing the same files weekly, cleaning the same columns, or building the same report monthly, you have been addressing the wrong issue. It is automation that differentiates an Excel person from one who knows how to use Excel efficiently. These Excel VBA and Power Query projects are aimed at removing the repetitive tasks in order to focus on analysis.

Power Query should always be your default option, if possible, as it is easy to maintain and no coding is required. VBA comes handy when there are specific needs for custom workflow, interaction, or automation beyond the capabilities of Power Query. For beginners in both tools, you can read more in depth about Automating Excel Reports and VBA in Excel .

11. Build a Refreshable Data Cleaning Pipeline with Power Query

Cleaning the same dataset every week is frustrating and unnecessary. Import the uncleaned data to Power Query, eliminate duplicates, solve problems in data formatting, split columns, and develop an automated refresh process, which refreshes your data every time you get new data. This requires some work the first time around, but thereafter it is simply a click away.

12. VBA Report Generator

Most monthly reports follow the same format, making the manual process of creating them tedious very quickly. Using VBA, you can create auto-formatted reports, update graphs and formatting, and export the workbook with just a click of a button. The more repetitive the reporting process, the more valuable this project becomes.

13. Automated Invoice System

Generating invoices can be considered as one task where human intervention is not always required. Develop an excel sheet that will capture all customer information, compute the total, tax, invoice number, and then create an invoice ready to print. Small companies use such tasks extensively.

14. Scheduled Email Reports

Imagine finishing your weekly report and having it delivered automatically instead of copying tables into Outlook every Friday. This project uses VBA to generate reports and send them to predefined recipients, making it a practical introduction to workflow automation inside Excel.

15. Excel Template Engine

Organizations use the same templates every single day regardless of whether they are preparing their expense statements, sales report sheets, or project management sheets. Rather than preparing new files for every occasion, develop a system that creates pre-formatting worksheets based on user inputs. This is a simple task but illustrates the concept of automation.

By the time you have gone through all the three levels, you won’t just be limited to basic functions. You will analyze data sets, create dashboards for visual display of the information, and automate certain processes, as most entry-level analysts do before moving on to solve business problems. More importantly, you will have some real-life examples of your Excel abilities rather than just function names on your CV.

Also check these recent updates by Excel. 

Data Cleaning: The Skill Every Project Secretly Tests

Excel work mostly doesn’t fail due to lack of formulas but because of the data itself being unclean. Duplicates, date inconsistencies, cell merges, blank entries, and even variations in spellings will give you a totally different analysis. This is why experts spend way more time on data preparation than making graphs and charts.

During interviews, speed of generating a Pivot Table is of little concern. What is of more importance is how you handle the unstructured data prior to analysis. This usually provides more insight into your analytical abilities than the generated dashboard.

Before starting any project, run through a simple cleaning checklist:

• Remove duplicate records.
• Standardize dates, currencies, and number formats.
• Replace or investigate missing values.
• Unmerge cells and give every column a clear header.
• Check for inconsistent spellings or category names.
• Convert numbers stored as text into numeric values.
• Look for obvious outliers before drawing conclusions.

None of these steps are particularly exciting, but they make every chart, Pivot Table, and dashboard more reliable. Treat Excel data cleaning as part of the analysis rather than a task to finish as quickly as possible, and the quality of your projects will improve noticeably.

Presenting Excel Projects (Portfolio & Interview)

A well-prepared workbook is just one aspect of a successful portfolio. The important thing about a particular project is the background to it, what the issue was initially and how you resolved it. That's what interviewers normally look for.

Don’t just upload your final dashboard. Instead, track the process by showing screenshots of the uncleaned data set and a screenshot of the final dashboard, along with a brief explanation of what kind of business problem you tried to solve through your dashboard. Sometimes a few observations go a long way, much further than any other chart.

When you talk about a project in an interview, start with explaining what problem was solved, then talk about the process of data cleaning, justification of the visualizations, and end with the business insights found. If you know the reasons for each step in the process, the spreadsheet will be a reflection of your thought process showing your Excel skills.

Once you've built a few strong Excel projects, expand your portfolio with SQL, Power BI, or Python to demonstrate that you can work across the modern analytics stack. 

You should also check out Excel help & learning by Microsoft Support for more examples and updates. 

For more such ideas, check out: Data Analyst Projects for 2026 (Beginner to Advanced)

When to Graduate: Excel → Power BI → SQL

Excel is where many analysts start their journey and with good cause. Excel shines at examining datasets, creating dashboards, and answering business questions quickly. However, as your datasets get larger and collaboration comes into play for reporting, you will start hitting limitations in Excel. Your workbooks will be slower, data refresh will take longer, and collaboration will get harder.

That's usually the point where Power BI becomes a better fit. Instead of creating static dashboards, you can connect directly to multiple data sources, refresh reports automatically, and build interactive dashboards that are easier to share across an organisation. The transition also feels natural because many of the skills you've already developed in Excel, working with tables, understanding KPIs, choosing the right charts, and thinking about business questions carry over directly.

SQL is the next piece of the puzzle. Rather than importing CSV files into Excel every week, you'll query the data directly from the database, retrieve only what you need, and pass it into Excel or Power BI for analysis. In practice, these tools complement each other rather than compete.

So instead of thinking in terms of Excel vs Power BI, think of them as different stages of the same workflow. Excel helps you understand the data, SQL helps you retrieve it, and Power BI helps you present it at scale. The strongest analysts know when to use each tool instead of trying to solve every problem with just one.

If you're planning a long-term career in analytics, you can check out this Power BI Roadmap after you can efficiently use Excel. And if you're ready to combine Excel, SQL, Power BI, and Python into a complete analytics toolkit, explore Scaler's Data Science Program to continue building production-ready analytical skills.

FAQs

Q1. What Excel projects should I include on my resume?

Aim for variety instead of quantity. A sales or HR analysis project demonstrates analytical thinking, Excel dashboard projects showcase reporting and visualization skills, and an automation project using Power Query or VBA highlights efficiency. Together, they paint a much stronger picture than several projects built around the same Excel features.

Q2. Is Excel enough to become a data analyst?

Excel is an excellent starting point because it teaches data cleaning, analysis, and reporting. However, most analyst roles also expect working knowledge of SQL and a visualization tool like Power BI. Build a strong Excel foundation first, then expand your skill set gradually instead of trying to learn everything at once.

Q3. Where can I find datasets for Excel projects?

Public datasets are the easiest place to begin. Kaggle offers datasets across sales, HR, finance, healthcare, and marketing, while government open-data portals provide real-world data for analysis. You can also create projects using your own budgeting, fitness, or expense data to make your portfolio more personal and practical.

Q4. Should I learn Power Query or VBA first?

Start with Power Query. It handles most modern data cleaning and transformation tasks without requiring code, making it useful for everyday reporting. Once you're comfortable with those workflows, move on to VBA Excel projects if you want to automate repetitive tasks, build custom macros, or create interactive Excel applications.

Q5. How should I present Excel projects in a portfolio?

A workbook alone doesn't tell the full story. Show the raw dataset, the cleaned version, and the final dashboard, then include a short explanation of the business problem, your approach, and the insights you uncovered. Recruiters are interested in your thinking as much as the finished spreadsheet.

Q6. What Excel skills do interviewers actually test?

Most interviews focus on practical skills rather than memorization. Be comfortable with data cleaning, Pivot Tables, XLOOKUP (or INDEX-MATCH), chart selection, and explaining the decisions you made during your analysis. Interviewers are often more interested in why you reached a conclusion than the formula you used to calculate it.