Resume Rewrite for Data Analysts

# Resume Rewrite for Data Analysts This is the written companion to your **1:1 resume rewrite session with Anuj** (book it from Job Assistance in your workspace). Read it first, draft your bullets using the formula below, and come to the session with a v1 - we'll sharpen it together, not start from a blank page. A great resume won't get you the job, but a weak one will quietly cost you interviews you'd have aced - and the reasons it gets rejected are almost always fixable in an afternoon. This guide fixes them. --- ## 1. What an analyst resume is actually for - A resume's only job is to **earn a 20-second human skim after surviving the ATS keyword pass**. It doesn't get you the job; it gets you the screen call. - One page. No photo, no DOB, no marital status, no "Objective" paragraph. (India-specific cleanup in §7.) - Reverse-chronological. Recruiters read top-down and stop early. - Structure, top to bottom: 1. Name + one-line headline ("Data Analyst | SQL · Python · Tableau") + contact (email, phone, LinkedIn, GitHub/portfolio link) 2. Skills (grouped, scannable) 3. Projects (for career-switchers/freshers, above work experience) 4. Work experience 5. Education 6. Certifications (optional, last) **Lead with your strongest evidence.** If your projects are better proof of analyst ability than your past jobs - true for most switchers and freshers - put Projects *above* Work Experience so the recruiter hits your proof in the first skim. Flip it only once you have analyst work experience more impressive than your projects. ## 2. The bullet formula (the core of this guide) Every bullet = **Action verb + what you did + tool/method + quantified impact**. > `Built` a `churn-prediction dashboard` `in Tableau` that `surfaced the 3 segments driving 60% of cancellations, cutting the monthly retention review from 2 days to 20 minutes`. - **Action verb** (past tense, strong): Built, Automated, Analyzed, Modeled, Reduced, Designed, Identified, Forecasted. Avoid "Worked on", "Helped with", "Responsible for". - **What + tool**: name the specific tool (SQL, Python/pandas, Tableau, Excel) - these are the ATS keywords. - **Quantified impact**: a number - %, time saved, ₹/$ moved, rows processed, decisions enabled. No real number? Estimate honestly and label the basis (§5). **Weak → strong:** | Weak | Strong | |---|---| | Worked on sales data in SQL | Wrote 15+ SQL queries joining 4 tables to segment 50K customers by lifetime value, flagging the top 8% who drove 40% of revenue | | Made a dashboard in Tableau | Built a Tableau executive dashboard tracking 6 KPIs that replaced a weekly manual Excel report, saving ~4 hrs/week | | Did data cleaning in Python | Automated a pandas pipeline that cleaned and deduplicated 120K messy rows, cutting prep time from 3 hrs to 5 min | | Analyzed customer churn | Modeled churn drivers on 12 months of subscription data in SQL + Python, isolating 3 behaviors that predicted most cancellations | ## 3. Turning your DA-track projects into resume bullets You finished three projects. Each becomes two or three bullets. Per project: 1. What was the business question? (the "so what") 2. What data + tools did you use? (the keywords) 3. What did you find / build? (the deliverable) 4. What would it have changed for a real team? (the impact - estimate honestly) **Illustrative examples from your track** - use the *real* numbers your own analysis produced, not these: - **Marketing Campaign Performance (W7):** *Analyzed multi-channel campaign data in SQL + Python to compute CAC and ROAS per channel, identifying the channels driving the majority of qualified leads at the lowest cost-per-lead.* - **Executive Dashboard (W8):** *Built an executive Tableau dashboard tracking core revenue, retention, and acquisition KPIs, replacing a manual weekly Excel report with a single self-serve view for leadership.* - **SaaS Churn & Retention Capstone (W9):** *Modeled churn and retention on 12 months of SaaS subscription data in SQL + Python, segmenting users by behavior to surface the cohorts driving most cancellations and the retention upside of targeting them.* > Link the GitHub repo for each project on the resume (shortened). Recruiters who click are pre-warmed. Make sure each README is recruiter-ready (your W7 Git lesson covered this). ## 4. Skills section - what to list and how Group, don't dump: - **Languages & Querying:** SQL, Python (pandas, NumPy) - **BI & Viz:** Tableau, Excel (PivotTables, lookups, Power Query) - **Stats & Analysis:** A/B testing, cohort analysis, regression basics, descriptive stats - **Workflow:** Git/GitHub, Jupyter Rules: - Only list what you can defend under questioning. The honesty test: *can I hold a five-minute conversation about this?* If you watched one tutorial and never used it on real data, leave it off - a single "tell me about your experience with X" will expose it and cost you credibility on everything else. - No skill bars / star ratings / "proficien