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7 Data Analyst - SQL, Python, and Dashboards Resume Templates with Example

By Ria · Last Updated On October 24, 2026
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This guide shows you how to build a clear, focused Data Analyst - SQL, Python, and Dashboards resume. You will learn how to present relevant evidence and compare professional formats for this target role.

A strong resume makes analysis easy to review. Organize your SQL and Python work, data cleaning, dashboard development, and findings into clear sections with concise bullets and results that you can support.

The example is presented in 7 resume layouts. Compare the PNG previews to see how each format handles skills, project experience, education, and certifications, then open an example in the editor to customize it.

What Makes a Great Data Analyst Resume in 2026

A strong Data Analyst resume is easy to scan and shows how you cleaned data, checked its quality, and explained useful patterns. Clear headings and focused bullets help readers find relevant evidence quickly.

Include SQL, Python, Excel, Tableau, and Power BI when they match your work. Describe projects with specific methods, dataset details, dashboards, and supported results, while noting limits where they matter.

Note: Resume examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organizations do not imply affiliation or endorsement.

#1 Simple One Column Format Resume Example

The one-column body places resume content in a single sequence, a practical choice for readers who prefer a direct top-to-bottom review.

 
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Why This Format Works

  • Experience, education, and skills follow one reading path, helping reviewers track the document.
  • Consistent section order lets reviewers move from career history to supporting details.
  • Project descriptions stay in sequence without competing with a second column.

Best For

  • Top-to-bottom review preferences
  • Resumes with detailed project records
  • Career histories with several sections
  • Section-by-section reading
  • Text-first resume presentation

Resume Keywords

  • statistics
  • SQL
  • Python
  • Data analysis
  • Used Excel to cross-check summary tables
  • data cleaning
  • Data Visualization
  • reporting dashboards
  • Tableau
  • Power BI
  • business insights
  • data storytelling
  • turning spreadsheet data and business questions into clear analysis
 

#2 Simple Two Column Format Resume Example

Two columns provide parallel areas for resume content, helping readers review related sections without following one long vertical sequence.

 
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Why This Format Works

  • Distinct areas let reviewers find skills and education alongside the broader experience record.
  • Experience details can share space with supporting sections, limiting uninterrupted vertical scanning.
  • Separate reading areas make project evidence and credentials easier to revisit.

Best For

  • Parallel content organization
  • Resumes with varied sections
  • Quick navigation between content areas
  • Experience paired with supporting details
  • Readers who prefer side-by-side information

#3 Plain One Column with Shaded Labels Resume Example

A single-column body keeps resume content in a direct sequence, making this format a good choice for readers who prefer to review each section in turn.

 
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Why This Format Works

  • The single reading path keeps career history, education, and skills in clear sequence.
  • Reviewers can follow project evidence without shifting between parallel content areas.
  • A continuous vertical order suits resumes with several distinct sections.

Best For

  • Sequential resume review
  • Resumes with several content sections
  • Project records in reading order
  • Direct experience-to-education progression
  • Simple text-led presentation
 

#4 One Column Format with Photo Right Resume Example

A right-side photo accompanies the single-column resume body, suiting readers who want a portrait while keeping the text in one sequence.

 
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Why This Format Works

  • One continuous column presents experience, education, and skills in a familiar order.
  • The portrait stays apart from the text, keeping resume sections in their reading sequence.
  • Project descriptions and credentials remain within the same vertical flow.

Best For

  • Right-side portrait preference
  • Single-sequence resume reading
  • Resumes with multiple sections
  • Text paired with a personal photo
  • Project details in a continuous flow
 

#5 Clean Two Column Sidebar Format Resume Example

The two-column body gives resume content parallel areas, a useful option for readers who want distinct sections visible together.

 
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Why This Format Works

  • Parallel areas help reviewers locate skills and education while keeping experience in view.
  • The two-column reading order reduces the need to move through one long vertical sequence.
  • Project evidence and credentials can occupy distinct areas of the resume.

Best For

  • Parallel resume section review
  • Experience with supporting details
  • Readers who value quick navigation
  • Several content types in one document
  • Side-by-side information preferences
 
 

#6 Two Column Format Resume Example

This two-column layout places resume information in parallel areas, giving readers a way to move between sections without relying on one long text sequence.

 
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Why This Format Works

  • Reviewers can move between experience and supporting resume sections with less vertical scanning.
  • Separate content areas give education and credentials clear places in the overall reading order.
  • The parallel structure helps readers return to project details while reviewing other sections.

Best For

  • Resumes with parallel content areas
  • Frequent movement between sections
  • Project details alongside other records
  • Readers seeking a shorter vertical scan
  • Multiple resume categories
 

#7 Photo Header Two Column Format Resume Example

A photo appears with a two-column resume layout, offering a portrait-based presentation while keeping the document content divided into parallel areas.

 
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Why This Format Works

  • Two columns help reviewers move between experience and supporting details without one long scan.
  • Parallel areas give skills, education, and credentials distinct places in the document.
  • The photo adds a portrait option while the resume information remains in two columns.

Best For

  • Photo-based resume presentation
  • Two-column section navigation
  • Experience with supporting information
  • Readers who prefer parallel content
  • Portraits alongside a divided resume
 
 
 

Why Build Your Data Analyst Resume with ResumeInMinutes

ResumeInMinutes lets you compare resume image previews and open an example in the editor. Use the format that presents your analysis, project work, skills, and education clearly.

  • ATS-readable section structure
  • Clear skills organization
  • Project-focused experience
  • Space for tools and methods
  • Readable dashboard and analysis evidence
  • Resume customization in the editor

Keep the content specific to the role. Use accurate keywords and explain how your SQL, Python, spreadsheet checks, or dashboard work helped you examine data and communicate findings.

 

See Also

 

Frequently Asked Questions

Are these Data Analyst resume templates ATS-friendly?

The formats use readable headings and organized sections to support clear resume review. Include relevant terms such as SQL, Python, data cleaning, Tableau, Power BI, and reporting dashboards when they accurately describe your experience.

Do the examples show different resume designs?

Yes. The page provides PNG previews of different layouts for the same Data Analyst resume example. You can compare the designs and open an example in the editor.

What should a Data Analyst resume include?

Include a focused summary, relevant experience or projects, skills, education, and certifications. Use concise bullets to explain how you worked with data, checked its quality, built reports or dashboards, and shared findings.

Which resume style works well for a Data Analyst role?

A simple layout with clear headings and a logical section order is a practical choice. It helps readers find skills, project details, and results without distracting design elements.

How can I show relevant experience if I am early in my career?

Use class, research, or portfolio projects to show how you used tools and methods. Explain the dataset, your analysis steps, any checks you made, and what you learned, without overstating the results.

Should I include a photo on my resume?

For U.S. applications, a resume without a photo is generally the safer choice. Follow local norms if you apply in another country.

ATS score and resume checks

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ATS Score 96%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your resume is used. This is not a match to a specific vacancy.
  • Text-based resume: 20%. The text resume assessed here receives 100 for this part.
  • Resume quality: 20%. We start at 100 and subtract points for issues found by our resume checks, down to a minimum of zero.
  • Keyword coverage = round(100 × confirmed matches ÷ total keywords). Exact matches and supported equivalents verified by AI receive full credit; unsupported keywords receive none. Live text checks use the saved equivalents and do not request a new AI review. Without keywords, no overall ATS score is available.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based resume 20% · Resume quality 20%

Keyword coverage: 13 of 14 (93%) · Resume quality: 100%

Resume quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Connor Huff
  • ✓Email format: connor.huff@example.com
  • ✓Phone format: (608) 555-XXXX
  • ✓Experience 1: Job title: Data Analyst, Applied Portfolio Project
  • ✓Experience 1: Company name: Student Data Lab
  • ✓Experience 1: Work location: Madison, WI
  • ✓Experience 1: Employment dates: Jan 2026 - Oct 2026
  • ✓Experience 1: Description or achievements: Present
  • ✓Experience 2: Job title: Data Analyst, Capstone Project
  • ✓Experience 2: Company name: University Project
  • ✓Experience 2: Work location: Madison, WI
  • ✓Experience 2: Employment dates: Jan 2025 - May 2025
  • ✓Experience 2: Description or achievements: Present
  • ✓Experience 3: Job title: Data Analyst, Research Project
  • ✓Experience 3: Company name: Academic Research
  • ✓Experience 3: Work location: Madison, WI
  • ✓Experience 3: Employment dates: Sep 2024 - Dec 2024
  • ✓Experience 3: Description or achievements: Present
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based Resume 100% · Weight 20%

Parsed text
Connor Huff
(608) 555-XXXX
connor.huff@example.com
connorhuff
Madison, WI 53703
SUMMARY
Recent statistics graduate and entry-level Data Analyst with practical experience turning spreadsheet data and business questions into clear analysis, dashboards, and useful findings. Use SQL and Python to clean and explore datasets, and Excel to check results before communicating patterns through Tableau and Power BI. Applied statistics and data management coursework supports careful interpretation of measures and transparent analysis. Across retail operations, service operations, and survey research projects, present findings in concise reports and data storytelling briefs that help nontechnical audiences consider business insights. Document data cleaning decisions, analytical choices, and limitations so conclusions remain clear and grounded in the available evidence.
EDUCATION
Bachelor of Science
2025
University of Wisconsin-Madison
Madison, WI
Coursework: Applied statistics, probability, data management, statistical methods
TECHNICAL SKILLS
Query Languages: SQL, T-SQL, PostgreSQL SQL
Programming Languages: Python, R, JavaScript
Python Data Libraries: pandas, NumPy, SciPy
Statistical Tools: statsmodels, scikit-learn, Jupyter
Spreadsheet Software: Microsoft Excel, Google Sheets, LibreOffice Calc
Business Intelligence: Power BI, Tableau, Looker Studio
Data Visualization Libraries: Matplotlib, Seaborn, Plotly
Relational Databases: PostgreSQL, MySQL, SQLite
Data Formats: CSV, JSON, Microsoft Excel XLSX
Notebook Platforms: Jupyter Notebook, Google Colab, RStudio
SKILLS
Data analysis
SQL
Python
Excel
Spreadsheets
Data cleaning
Data visualization
Dashboards
Tableau
Power BI
Statistics
Data quality
Business analysis
Data storytelling
Communication
EXPERIENCE
Data Analyst, Applied Portfolio Project
Jan 2026 - Oct 2026
Student Data Lab
Madison, WI
Analyzed a 24,000-row sample of retail operations data with SQL and Python, resolving inconsistent dates, duplicate records, and missing category values before comparing weekly demand patterns.
Created reporting dashboards in Power BI and Tableau to show product and fulfillment trends, adding clear labels and filters so viewers could explore results without relying on static spreadsheets.
Used Excel to cross-check summary tables, then presented a data storytelling brief that connected observed order patterns with practical questions for business operations planning.
Data Analyst, Capstone Project
Jan 2025 - May 2025
University Project
Madison, WI
Converted a spreadsheet-based service operations dataset into a documented analysis workflow, using SQL queries and Python checks to clean records and confirm consistent definitions for key measures.
Applied descriptive statistics to compare workload patterns across service categories, highlighting outliers and recurring trends for a faculty-reviewed business insights presentation.
Built a Tableau dashboard and accompanying written report, explaining the data visualization choices and limitations in accessible language for a nontechnical audience.
Data Analyst, Research Project
Sep 2024 - Dec 2024
Academic Research
Madison, WI
Organized a course research dataset in Excel and Python, documenting data cleaning decisions and checking summary values before statistical analysis.
Examined patterns in a sample of 1,500 survey responses using foundational statistical methods, then created charts and a concise presentation to communicate findings and caveats.
LEADERSHIP, AWARDS & RECOGNITION
Dean's List, Fall 2023 and Spring 2024.
Received faculty recognition for clear communication at the Statistics Capstone Showcase in 2025.
Tutored introductory statistics study sessions at the University of Wisconsin-Madison, 2024-2025.
Coordinated Statistics Student Association workshops for student-led study and presentation sessions, 2024-2025.
CERTIFICATIONS
Google Data Analytics Professional Certificate
2025
Microsoft Learn, Power BI Data Analyst learning path
2026
PROFESSIONAL AFFILIATIONS
Member, American Statistical Association, engaging with professional resources and developments in statistics.
Member, Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining (ACM SIGKDD), connecting with the data mining community.
LANGUAGES
English (Native)
Spanish (Intermediate)

Matched keywords (13)

  • statisticsOriginal keyword in JD: Bachelor's OR Master's in Computer Science OR Engineering OR Mathematics OR Statistics
  • SQL
  • Python
  • Data analysis
  • Used Excel to cross-check summary tablesOriginal keyword in JD: Spreadsheet analysis
  • data cleaning
  • Data Visualization
  • reporting dashboards
  • Tableau
  • Power BI
  • business insights
  • data storytelling
  • turning spreadsheet data and business questions into clear analysisOriginal keyword in JD: Business question analysis

Missing keywords (1)

  • Pattern identification and interpretation
Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

Resume and cover-letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organization.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that resume or cover-letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover-Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on resume formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover-letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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