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Read More →Download the data analyst skills checklist PDF from uncodemy: technical, analytical and business skills as a tickable list with levels.
This page offers uncodemy's data analyst skills checklist as a PDF download. The checklist groups the skills employers commonly expect from a junior data analyst into technical, analytical and communication areas, and suggests a way to prove each one. Use it to find gaps, plan your practice and prepare for interviews.
Start with the main skills checklist PDF. The extra cards below are for any companion files uncodemy publishes on this topic — download only what you need.
Technical, analytical and communication skills grouped into a tickable list, with what good looks like for a junior and the proof you can show. Includes prioritising gaps, a four-week plan to close one gap, overlooked skills, a simple scoring scale and CV tips.
An optional second file on the same topic. Download this only if uncodemy publishes an additional companion PDF to the main checklist; otherwise skip it and use the skill tables on this page instead.
An optional third file on the same topic. Same rule: download it only if uncodemy publishes an extra PDF that genuinely fits this checklist; otherwise use the tables and scoring scale on this page.
Each download is a separate file. Show only as many cards as there are real PDFs — each with its own title, description, page count, size and file.
A skills checklist is a mirror, not a scorecard. Its purpose is to help you see where you stand, choose what to learn next and decide what evidence to show an employer. Go through it honestly and mark each skill as not started, learning, or can do independently.
For every skill you mark can do, write down the proof: a project, a query, a dashboard or a written case. A skill without proof is only a claim. Review the list every month and update it as your confidence changes.
| Skill | What good looks like for a junior | Proof you can show |
|---|---|---|
| Excel | Formulas, lookups, pivot tables, charts, basic Power Query | A cleaned workbook with a pivot-based summary |
| SQL | Joins, grouping, filtering, subqueries, CTEs, window functions | A set of queries answering business questions |
| Python or R (basic) | Load, clean, summarise and chart data with pandas | A notebook with a short analysis |
| BI tool | Build a clear dashboard with slicers and KPIs | A published or screenshot dashboard |
| Statistics | Summaries, distributions, correlation, simple tests | A write-up explaining a result in plain words |
| Data cleaning | Handle missing values, duplicates, types and text | A documented cleaning log |
| Version control | Basic Git for notebooks and queries | A public repository with a README |
| Skill | What good looks like | Proof |
|---|---|---|
| Problem framing | Turn a vague request into a metric and a plan | A written problem breakdown |
| Critical thinking | Question the data source, definitions and assumptions | A note listing assumptions and checks |
| Hypothesis testing | Propose causes and test them with data | An analysis that ruled out causes |
| Attention to detail | Reconcile totals and catch errors | A validation step in your project |
| Business understanding | Know how the domain makes money and what it tracks | A project written around a real business question |
| Skill | What good looks like | Proof |
|---|---|---|
| Data storytelling | A clear message, visual and recommendation | A one-page summary or slide |
| Written communication | Short, accurate emails and documentation | README files and project notes |
| Presenting | Explain results to non-technical people | A recorded or live walkthrough |
| Stakeholder questions | Ask clarifying questions before starting | A list of questions for a project brief |
| Time management | Estimate and deliver small pieces on time | A project plan with milestones |
You cannot learn everything at once, so rank the gaps by how often job descriptions mention them and by how foundational they are. For most junior data analyst roles the order of value is SQL, Excel, a BI tool, statistics, then Python. Within each skill choose depth over breadth: it is better to write fifty good SQL queries than to read about ten database engines. Set a small weekly target, such as two SQL practice sessions and one chart review, and track it.
Write your CV bullets around evidence rather than adjectives. Instead of "proficient in SQL", write "analysed order data with joins and window functions to find the top customers by month". Each bullet should name the action, the tool and the result. If a skill on the checklist has no proof, build a small project first and then add it to your CV.
| Level | Meaning | What to do next |
|---|---|---|
| 0 – Not started | You have not practised the skill | Choose one beginner resource and finish the first exercises |
| 1 – Learning | You can follow a tutorial but need help alone | Repeat tasks without looking at the solution |
| 2 – Independent | You can do standard tasks without help | Build a project that proves it |
| 3 – Confident | You can explain it and handle unusual cases | Teach it to someone else or write about it |
New analysts are often judged less by the hardest query they can write than by how reliably they work with others. Reply to requests promptly, confirm your understanding before starting, give honest time estimates and flag problems early. Share a draft before polishing it for days, because early feedback prevents wasted effort. Keep notes so that your work can be reviewed and repeated. These habits are free to practise in any project, and they are part of what employers mean by professionalism.
Ready to close the gaps on this checklist with a mentor? Explore uncodemy's Data Analytics course and learn from industry trainers.
