Data Analyst Resume Example

Yes, if you have relevant public repositories showing SQL analyses, Python notebooks, dbt projects, or visualization work. A GitHub profile with well-documented analytical projects demonstrates technical competence beyond what resume bullets can convey. Even 2-3 clean, well-commented repositories can significantly strengthen your application, especially for competitive roles.

Frame your Excel experience as analytical thinking and business acumen, then highlight your SQL and Python skills prominently. On your resume, describe the business problems you solved, not just the tools you used. If you built complex Excel models, reframe them: 'Built a revenue forecasting model tracking 12 variables across 4 business units' demonstrates analytical skill regardless of the tool.

SQL is the non-negotiable foundation — virtually every data analyst role requires it. Python is increasingly expected but still secondary to SQL for most analyst positions. Lead with your SQL proficiency and demonstrate Python skills for statistical analysis, automation, or machine learning applications. If the job description emphasizes one over the other, mirror that emphasis.

Frame your bullets as mini-stories: the question you investigated, the insight you discovered, and the action it drove. For example, 'Discovered that enterprise customers who used Feature X within 7 days had 2.5x higher expansion revenue — insight that reshaped the customer success playbook and increased upsell rates by 19%.' The narrative arc itself demonstrates your storytelling ability.

Certifications are helpful for career changers and junior analysts — they signal baseline competency and learning initiative. For experienced analysts, they matter less than demonstrated impact. Include them in a concise 'Certifications' section, but don't let them displace space for high-impact experience bullets. Google Data Analytics, IBM Data Analyst, and Tableau Desktop Specialist are the most recognized.

Focus on 2-3 of your most impactful dashboards rather than listing every dashboard you've ever built. For each, specify: what it tracked (metrics), who used it (audience), and what decisions it enabled (impact). One dashboard that influenced a $1M budget decision is worth more on your resume than ten routine weekly reports.

Basic ML literacy is increasingly valuable but not required for most analyst roles. Understanding regression, classification basics, and when to recommend an ML approach versus a simpler analysis sets you apart. If you've built simple predictive models (churn prediction, demand forecasting), include them — but don't overstate your ML skills if the role is analytics-focused.