How to Build a Data Analyst Portfolio with Power BI and GitHub

If you’re trying to break into data analytics or level up your career, let me be very clear with you: Recruiters don’t hire skills. They hire proof. A data analyst portfolio is that proof.

Certifications help, resumes open some doors, but your Power BI dashboards and GitHub projects actually show what you can do. They demonstrate your real abilities, not just what you claim to know.

In this guide, I’ll walk you through exactly how to build a practical, recruiter-ready data analyst portfolio using Power BI and GitHub, step by step. I’ll also explain why each step matters so you build this the right way—not just in a way that looks flashy but lacks substance.

Why a Data Analyst Portfolio Matters More Than a Resume

Here’s the simple truth: Resumes tell people what you know. Portfolios show them what you can do.

Hiring managers want to see:

How you tackle real data problems end to end, clean and transform messy data, build clear dashboards, and communicate insights that drive decisions.

A strong portfolio can:

Offset your lack of formal work experience. Support a career switch from another field. Prove your hands-on Power BI and analytical skills. Make your GitHub profile meaningful and professional.

Related Read: Data Preprocessing in Analysis: Encoding, Scaling, Transformation

What Makes a Strong Data Analyst Portfolio

Before we get into tools, you need to understand what makes a portfolio actually strong.

A good portfolio must show:

End-to-end thinking, taking raw data all the way to actionable insights. Business understanding, not just technical skills. Clean visuals and clear storytelling. Proper documentation that explains your process.

Remember this: your portfolio is not about how many projects you have—it’s about how well those projects are executed.

The Role of Power BI and GitHub in Your Portfolio

Let me explain why you need both of these platforms.

Power BI shows:

• Your data modeling abilities.
• Your DAX skills for calculations.
• Your visualization design choices.
• Your business storytelling capability.

GitHub shows:

• Your project structure and organization.
• Your data cleaning logic and process.
• Your documentation skills.
• Your understanding of version control.
• Your professional discipline.

When you use them together, they create a complete picture of you as an analyst.

Step 1: Choose the Right Portfolio Projects

This is honestly where most beginners go wrong, so pay attention.

How many projects do you need?

Three to five high-quality projects are enough. Quality beats quantity every single time.

Types of projects you should include:

Sales analysis (revenue trends, product performance), Customer churn analysis (retention insights), Marketing performance (campaign effectiveness), Financial performance (profit margins, budget analysis), Operations or HR analytics (efficiency metrics).

My advice: Avoid using “toy” datasets with no real-world context. Always frame each project around an actual business problem that needs solving.

Related Read: Excel for Data Analysis (Advanced Excel Skills)

Step 2: Structure Each Project Properly

Every single one of your portfolio projects should follow a consistent structure. This shows professionalism.

Recommended project structure:

Problem statement that explains what you’re solving. Dataset description including source and what it contains. Data cleaning steps you took. Data modeling approach you used. Analysis approach and methodology. Power BI dashboard with clear visuals. Insights and business recommendations.

This structure tells recruiters one important thing: “This person thinks like a real analyst, not just someone who learned tools.”

Step 3: Data Cleaning and Preparation (Show This Clearly)

Never hide your data cleaning work. This is actually one of the most valuable things you can show.

What to include in your documentation:

• How you handled missing values and why.
• How did you remove duplicates?
• How you fixed incorrect data types?
• What calculated columns you created and why?

Example

If revenue values in your raw data appeared in different formats like $1,200, 1200, and $1,200.00, explain exactly how you standardized them into clean numeric format before starting your analysis.

My tip: Document your cleaning logic clearly in GitHub. This shows you’re ready for real-world work where data is always messy.

Related Read: Data visualization Fundamentals: How to Present Data Effectively

Step 4: Build Meaningful Power BI Dashboards

Your dashboard should answer business questions—not just look pretty.

Key dashboard elements to include:

• KPIs like Revenue, Growth Percentage, Profit.
• Trends over time showing patterns.
• Comparisons across Region, Product, or Category.
• Filters and slicers for interactivity.

Visualization best practices:

Show one clear insight per visual. Use clear, descriptive titles. Keep scales consistent across similar charts. Minimize clutter and unnecessary decorations.

My insight: A simple dashboard with strong, clear insights will beat a complex, confusing one every time. Don’t overthink the design.

Step 5: Use DAX Where It Adds Value

Don’t overuse DAX just to show off—but don’t avoid it either when it’s needed.

Examples of useful DAX measures:

Total Sales calculations. Year-over-Year growth comparisons. Rolling averages for trends. Profit margin calculations.

Example

Create a measure like this:

Average Order Value = Total Sales divided by Number of Orders

Then explain in your documentation why this metric matters for your analysis and what business questions it helps answer.

Related Read: DAX in Power BI: A Practical Guide for Beginners

Step 6: Publish and Export Power BI Work Properly

You can’t assume recruiters will download and open your .pbix files. Make it easy for them.

Best practices:

Upload your .pbix file to GitHub. Add clear dashboard screenshots. Write a short explanation for each visual. Include your key insights right below the dashboard images.

My advice: Treat each project like a complete case study, not just a file dump. Tell the story of your analysis.

Step 7: Create a Professional GitHub Repository

Your GitHub profile should look clean, organized, and intentional.

Recommended folder structure:

/data folder for raw and cleaned datasets. /powerbi folder for dashboard files. /documentation folder for analysis notes. README.md file with your project overview.

This structure shows you understand professional project organization.

Step 8: Write a Strong README File (This Is Very Important)

Your README file is where most candidates lose impact. Don’t let that be you.

Your README should include:

Project overview and context. Clear business problem statement. Tools you used (Power BI, Excel, SQL, Python). Key steps you followed in your analysis. Insights and actionable recommendations.

My tip: Write your README like you’re explaining your project to a hiring manager who has only five minutes. Be clear, direct, and business-focused.

Related Read: Data Sources and Collection Methods for Effective Data Analysis

Step 9: Highlight Business Impact

Remember: insights without impact don’t matter to businesses.

Example of weak insight:

“Sales declined in Q3.”

Example of strong insight:

“Sales declined by 12% in Q3, mainly due to reduced repeat customers in Region A. Improving customer retention in this region could recover approximately $8,000 in monthly revenue.”

See the difference? The second one shows analytical maturity and business thinking.

Step 10: Link Your Portfolio Everywhere

Make your portfolio easy to find. If a recruiter can’t find it quickly, they won’t look for it.

Where to add your portfolio links:

At the top of your resume. In your LinkedIn profile summary and featured section. In your GitHub bio. On your personal website if you have one.

My advice: If a recruiter can’t find your portfolio in 30 seconds or less, it probably won’t get reviewed. Make it prominent.

Common Portfolio Mistakes to Avoid

Let me share the mistakes I see all the time so you can avoid them:

Having too many low-quality projects instead of a few strong ones. Not providing any business context for your projects. Creating overcrowded dashboards that confuse rather than clarify. Skipping documentation entirely. Copy-pasting tutorial projects without adding your own analysis.

Avoid these mistakes, and you’ll already be ahead of most other candidates applying for the same roles.

What Recruiters Actually Look For in Your Portfolio

When recruiters review your portfolio, they’re looking for specific things:

Problem-solving mindset and structured thinking. Clean, logical approach to analysis. Clear business understanding. Strong communication skills. Consistency across all your projects.

Tools matter, absolutely. But your thinking matters more. Show both, and you’ll stand out.

Related Read: How to build a LinkedIn profile to attract recruiters

My Final Advice to You

Your data analyst portfolio is honestly your strongest career asset. Power BI shows how you analyze and visualize data. GitHub shows how you work professionally and document your thinking.

Build fewer projects, but build them really well. Explain your thinking process clearly. Always show business impact, not just technical skills. Do this consistently, and your portfolio will open doors—even if you don’t have years of formal experience yet.

Remember: you’re not just showing what tools you know. You’re proving you can solve real business problems with data. That’s what gets you hired.