Should I list Python or R if I only used it in a bootcamp?

List it if you can complete a real task with it — the interview test for analyst skills is practical. Pair every listed tool with one experience or project bullet that uses it ('Automated weekly revenue report in Python, saving 6 hours/week'). A tool named only in the skills list reads as padding; the same tool proven in a bullet reads as capability.

Can I check my resume against these rules for free?

Yes. Paste your resume text (or import the file) into KlarCV and the built-in ATS check scores parseability, keyword match and bullet strength — with concrete fix suggestions for each issue it finds. No account required during the test phase.

Technical Skills List for a Data Analyst Resume

Data analyst postings are keyword gauntlets: the ATS filters for exact tool names before any human reads your work. But 'technical skills' isn't one list — the right set depends on whether the role is BI-heavy, product-analytics or statistical. Below are the canonical skill lists by specialization, with the terms that actually appear in 2026 job ads.

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Core (expected on every data analyst resume)

Missing any of these on a mid-level application is a red flag:

SQL (name the dialect: PostgreSQL, BigQuery, Snowflake)
Excel (pivot tables, Power Query, advanced formulas)
Data visualization: Tableau or Power BI
Statistics fundamentals: hypothesis testing, regression
Data cleaning & transformation

Programming & analysis

The differentiator tier for mid/senior roles:

Python (pandas, NumPy, matplotlib) or R (tidyverse, ggplot2)
Jupyter notebooks
dbt for analytics engineering
Git for version control
APIs & web data extraction

Business & product analytics

Terms for product-analyst and growth roles:

A/B testing & experiment design
Cohort & funnel analysis
GA4 / Amplitude / Mixpanel
SaaS metrics: MRR, churn, LTV, CAC
Dashboarding for stakeholders

Emerging (mention if truthful)

Increasingly present in 2026 postings:

LLM-assisted analysis workflows
dbt / semantic layer tooling
Cloud platforms: AWS, GCP, Azure basics
Looker / LookML
Basic ML concepts (classification, forecasting)

Before / after: see the difference

Weak: "Skills: SQL, Python, Tableau, Excel, data analysis, reporting, communication" Strong: "Analysis: SQL (Snowflake, BigQuery), Python (pandas, NumPy), dbt · Visualization: Tableau, Looker · Methods: A/B testing, cohort analysis, regression · Domain: SaaS metrics, marketplace analytics" Why it works: The grouped version matches more parser keywords (specific dialects and libraries are separate index terms) and shows a hiring manager your actual depth in five seconds.

Frequently asked questions

Should I list Python or R if I only used it in a bootcamp?: List it if you can complete a real task with it — the interview test for analyst skills is practical. Pair every listed tool with one experience or project bullet that uses it ('Automated weekly revenue report in Python, saving 6 hours/week'). A tool named only in the skills list reads as padding; the same tool proven in a bullet reads as capability.
Can I check my resume against these rules for free?: Yes. Paste your resume text (or import the file) into KlarCV and the built-in ATS check scores parseability, keyword match and bullet strength — with concrete fix suggestions for each issue it finds. No account required during the test phase.

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