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Read More →Download the data analyst portfolio guide PDF from Uncodemy: what to include, how to present projects, README structure and review checklist.
This page offers Uncodemy's data analyst portfolio guide as a PDF download. A portfolio is the evidence behind your CV. The guide explains how many projects to include, what each write-up should contain, how to structure a repository and where to publish, so that a recruiter can understand your work in a few minutes.
The download below is its own file. It covers what to include, how to write up each project, a README structure and a checklist to review before you publish.
How many projects to include, what each write-up should contain, how to structure a repository and where to publish, so a recruiter can understand your work in a few minutes.
A recruiter reading a CV sees claims: proficient in SQL, strong analytical skills. A portfolio shows evidence. It lets someone who has never met you see how you think: how you frame a question, clean awkward data, choose a method, interpret results and communicate a recommendation. For candidates without experience this evidence is especially valuable, because it replaces work history with proof. A small, well-explained portfolio almost always beats a large, unexplained one, since reviewers have only minutes to spend.
| Item | Why it matters |
|---|---|
| Two to four projects | Enough to show range without diluting quality |
| A mix of skills | For example SQL analysis, a dashboard and a Python notebook |
| One end-to-end project | Shows the full cycle from question to recommendation |
| Clear README for each | Lets a reviewer understand it without running anything |
| Short profile page | Who you are, tools you use, how to contact you |
Plain text for a project repository. Replace the bracketed parts with your own details.
# Project title: [short, descriptive name]
## Business question
[One or two sentences: who needs what decision made?]
## Data
- Source: [link and licence]
- Size: [rows x columns]; grain: [one row = ...]
## Approach
1. Cleaning: [main issues and how you handled them]
2. Analysis: [queries or notebook sections]
3. Visualisation: [dashboard or charts]
## Key findings
- [Finding 1 with a number]
- [Finding 2 with a number]
## Recommendation
[Action, owner, timing, how success is measured]
## Limitations
[Missing data, assumptions, what you would do next]
## How to run
[Tools, versions, steps]Choose projects that answer a real business question instead of showing off a technique. A project on why delivery times vary, with a clear recommendation, is stronger than a technically impressive model with no stated purpose. Prefer datasets that are less overused, or add your own angle to a common one, such as combining two sources or focusing on a specific local context. Make sure you can explain every step, since interviewers often ask you to walk through your work and probe your choices.
Update your portfolio as you improve. Replace the weakest project with a better one, fix anything that no longer works and add a short note on what you learned. Treat it as a living page that reflects your current level, because that is how a recruiter will read it.
Reviewers decide in seconds whether to keep reading. Make the top of every project page count: one sentence on the business question, one on the result, a single clear chart or dashboard image and a link to details. Avoid starting with installation instructions or a list of libraries. A reader who understands the point in ten seconds will read on; a reader who sees only technical setup will leave.
| Criterion | Weak | Strong |
|---|---|---|
| Question | Describe the dataset | Solve a stated business problem |
| Cleaning | Hidden or skipped | Documented with before and after counts |
| Analysis | Only descriptive charts | Compares, explains and tests |
| Communication | Code only | Clear findings and recommendation |
| Reproducibility | Cannot be run | README with steps and versions |
Prepare a two-minute walkthrough for each project: the problem, what you did, the result and what you would do differently. Practise saying it without reading. Interviewers often choose one detail, such as how you handled missing values, and ask you to defend it. Having a clear reason for each decision, even if the decision was imperfect, shows maturity. Bring one project that you can discuss in depth, including a mistake you found and fixed, since honest learning impresses more than a flawless story.
Ready to build a portfolio recruiters actually read? Explore Uncodemy's Data Analytics course and learn from industry trainers.