# Data Science Portfolios That Show You Can Ship--Not Just Model

Resumes that just list Python and SQL get filtered. Build a data science portfolio plus ATS resume from one profile. Models, impact, and clarity in 10 minutes.

## Why Data Scientists Benefit from a Portfolio

A resume says you know Python and SQL. A portfolio shows how you use them: what problem you tackled, how you approached it, what you found, and what changed as a result. That story is what separates candidates who "do data science" from those who drive decisions.
Many data science roles now expect some evidence of applied work--side projects, Kaggle, or work samples. A single place that curates your best work and explains the "so what" makes you easier to evaluate and more memorable.

## What to Put in a Data Science Portfolio

Include 3-5 projects that show range: e.g. prediction, inference, visualization, or experimentation. For each, cover: business or research question, data and methods, key results, and impact or recommendations. Link to code (GitHub, Colab) and any live dashboards or apps.
Write for a mixed audience. Technical reviewers will look at your code; product and hiring managers will read your summaries. Clear project descriptions and takeaway sections help everyone understand your contribution.

## Balancing Depth and Clarity

You don't need to publish a paper for every project. Well-documented notebooks, a short write-up, and a clear outcome are enough. Focus on projects that map to the roles you want--e.g. ML engineering vs. analytics vs. research--and keep the portfolio updated as you complete new work.
If you have blog posts, talks, or open-source contributions, link them. They signal communication skills and thought leadership, which matter for senior and staff-level roles.

## Why FolioX

FolioX gives data scientists a clean portfolio and resume in one place. Showcase your projects and impact with a professional layout, link to GitHub and demos, and pair it with an ATS-optimized resume so recruiters and hiring managers get the full picture.


## FAQ

### What should a data science portfolio include?

3-5 projects with clear problem, approach, results, and impact. Link to code and any dashboards or apps. Add a short About and resume. Tailor project choice to the roles you want (ML, analytics, etc.).

### Do I need a portfolio for data science jobs?

Many roles now expect evidence of applied work. A portfolio that curates your best projects and explains your thinking gives you an edge and makes it easier for hiring managers to assess fit.

### How do I present code in a data science portfolio?

Link to GitHub or Colab and provide a short written summary per project. Not everyone will run your code; the narrative (problem, method, result, impact) should stand on its own.

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