7 Real Data Science Portfolio Examples Worth Studying (and What Each One Proves)

Updated October 14, 2026 · 14 min read

Quick answer: The seven data science portfolios below are real, live pages belonging to working practitioners — not mockups — and each one solves a different problem. They break into seven archetypes: narrative-per-project, navigable project index, paper-backed depth, multi-track, cadence, authority-led, and minimum viable. The right one for you is set by your career stage, not by taste. And the pattern that surprised us: almost none of them lead with a business metric, even though nearly every guide says they should. What they all lead with is something a stranger can click and verify. If you want a single annotated walkthrough instead of a comparison, see the data science portfolio example; if you work on dashboards and reporting rather than models, start from the data analyst portfolio example.

Image credit: Olia Danilevich / Pexels

Key Takeaways

Stop asking which portfolio is best and ask which archetype matches where you are. A portfolio's job changes as your evidence changes: early on it has to show you can reason, in the middle it has to be navigable, and eventually it stops being a project list altogether.

Table of Contents

• What makes a data science portfolio example worth studying
• 1. Natassha Selvaraj — a writeup behind every project
• 2. Andrey Lukyanenko — breadth that stays navigable
• 3. Tatev Karen Aslanyan — the case study as the artifact
• 4. James Le — separate tracks for a split career
• 5. Yu Dong — consistency as the proof
• 6. Sajal Sharma — when authority replaces the project list
• 7. Chris Tran — the minimum that still clears the bar
• What all seven share — and the one thing almost none do
• Which archetype should you copy?
• Two more, built from a resume
• Getting yours live this week
• Frequently asked questions
• Useful sources and further reading

What Makes a Data Science Portfolio Example Worth Studying

Most "data science portfolio examples" lists are really template galleries. They show you layouts, which is the least transferable part of the whole exercise. A layout you can copy in an afternoon; the judgement about what to put in it is the part that takes people years to get right.

So the seven below were chosen on a different test. Each one had to be live and openable at the end of September 2026 — no archived links, no screenshots of pages that no longer exist. Each had to belong to a real named practitioner whose work you can independently check. And each had to be structurally different from the others, because seven variations on the same project grid would teach you nothing that one of them wouldn't.

What emerged was a spread that maps almost cleanly onto career stage. That is the useful part. A portfolio is not a fixed artifact you build once and polish forever; it is a response to whatever evidence you currently have and whatever doubt the reader currently holds. The doubt a career switcher has to defeat ("can this person actually reason about data?") is not the doubt a staff-level practitioner faces ("is this person worth an hour of my time?"), and the page that answers one does a poor job of answering the other.

1. Natassha Selvaraj — A Writeup Behind Every Project

natassha.github.io/natasshaselvaraj opens with one line that does a lot of work:

I am a data scientist with a passion for storytelling.

The page then earns that claim rather than just asserting it. Eight projects are listed, and every single one carries two links: a blog post explaining it, and either a live app or the source on GitHub. A quiz app that asks whether a face is real or AI-generated. A celebrity facial-recognition tool you can upload a photo to. An analysis of gender disparity in Hollywood built around the Bechdel test. A decision-tree pipeline on the Framingham Heart Study. A scraper that ranks online data science courses by review sentiment. A Harry Potter personality classifier wrapped in a Dash interface.

Notice what that pairing does. The blog post proves she can explain a decision to a non-specialist; the demo proves the thing actually runs. Together they answer the only two questions a hiring manager has about a self-directed project, and they answer them without the reader having to open a notebook and read code. There are no revenue figures anywhere on the page, and it does not need them — the projects are personal, not commercial, and pretending otherwise would ring false.

What to copy: the discipline of never shipping a project link on its own. If a project is worth listing, it is worth 400 words explaining why you built it and what surprised you. If it isn't worth 400 words, cut it from the portfolio.

2. Andrey Lukyanenko — Breadth That Stays Navigable

erlemar.github.io is the opposite strategy executed well. It is titled, plainly, "Data science portfolio by Andrey Lukyanenko," and it holds roughly forty projects — the sort of volume that usually turns a portfolio into an unreadable dump.

It stays readable because of one decision: the projects are grouped by problem type, not chronology. Stand-alone projects, Kaggle competitions, Kaggle kernels, then classification, regression, natural language processing, clustering, neural networks, data exploration, and recommendation systems. A hiring manager filling an NLP role scrolls to the NLP heading and ignores the other nine sections. That is the entire trick, and almost nobody does it.

The credibility comes from competitive results rather than business outcomes: a bronze medal in the Avito demand prediction competition at 131st place, third place in a Kaggle competition on Russian receipt categorization, and a Google Pixelbook awarded for the second-most-upvoted kernel in the DonorsChoose.org challenge. Most projects link out to both a GitHub repository and an nbviewer rendering of the notebook, with a handful running as live demos — a digit-recognition site and a Telegram chatbot among them.

What to copy: if you have volume, impose taxonomy. Group by the kind of problem solved so a reader can self-select in one scroll. And be specific about competitive placements — "131st place, bronze" is more credible than "top performer," precisely because it is unflattering enough to be true.

3. Tatev Karen Aslanyan — The Case Study as the Artifact

Tatev Karen Aslanyan's data science portfolio repository takes the idea of a project writeup and pushes it up a tier: several projects ship with a full case-study paper as a PDF alongside the code.

The repository is organised into research projects, machine learning case studies, and statistics case studies. The research entry is a new recommender algorithm, LDA-LFM, combining a rating model with topic modelling over textual reviews and user and item characteristics. The machine learning set runs through price prediction with LSTM layers, image recognition on an 8,000-image cat-and-dog set, customer churn prediction with a neural network, a top-N collaborative-filtering recommender, and anomaly detection using Isolation Forest and PCA. The statistics set is the part most portfolios skip entirely — ranking US cities with PCA, factor analysis and canonical correlation; comparing single against multiple imputation under different missingness mechanisms; building outlier-resistant classifiers with FastMCD.

Underneath it sits the strongest verifiability signal available to anyone: research accepted at the 36th ACM/SIGAPP Symposium on Applied Computing, published in the ACM Digital Library. A paper cannot be exaggerated. Someone else already checked it.

What to copy: the statistics section, even if you never write a paper. Most data science portfolios are all modelling and no inference, which quietly tells an interviewer you may not know when a model is inappropriate. A short piece on missing-data handling or an outlier-robustness comparison distinguishes you from a very large field.

4. James Le — Separate Tracks for a Split Career

jameskle.com belongs to someone whose career refuses to sit in one box, and the site is built around that fact rather than fighting it. The self-description is a path rather than a title: "Data Journalism -> Data Science -> ML Research -> Developer Relations," under the motto "Constraints Drive Creativity."

The navigation is the lesson. Work Experience, AI Research, Presentations, Data Portfolio, Data Journalism, Coding Portfolio, MOOCs, Undergraduate Papers, and a podcast — Datacast — interviewing other data practitioners. Note that "Data Portfolio" and "Coding Portfolio" are separate sections. A less confident site would have merged them into one "Projects" page and forced every visitor to sift. Splitting them means a research lead and an engineering manager each land somewhere relevant.

The current role, developer experience lead at Twelve Labs working on video understanding models, sits alongside earlier work in product growth and in bridging research to production. The through-line is not a single specialism; it is a person who repeatedly translates between technical depth and an audience — which is precisely what the portfolio structure demonstrates by existing in that shape.

What to copy: if you genuinely operate across tracks, resist the urge to flatten. One "Projects" page that mixes a research paper, a production service and a conference talk reads as unfocused. Three labelled sections read as range.

5. Yu Dong — Consistency as the Proof

yudong-94.github.io/personal-website presents itself as "Data Science & Analytics · Data Storyteller," and its structure is unusual: Vizzes, Articles, Reading Notes, Tags, About. No project case studies in the conventional sense at all.

What it has instead is duration. The visualisation collection has run weekly since 2018 and is in its eighth year. Reading notes appear on a bi-monthly rhythm. Articles are current rather than archival — recent pieces cover using agent skills in data science and applying AI across the full data science workflow, which places the author in the present conversation rather than in 2019.

This is a form of evidence no individual project can produce. A single polished case study tells a reader you can do good work once, possibly with help, possibly over three months. Eight years of dated weekly output tells them what you are like on an ordinary Tuesday. For any role where the real question is reliability — analytics, reporting, anything embedded with a business team — that signal is worth more than a flashier one-off.

What to copy: put dates on things and keep a visible cadence, however modest. One short post a month, dated, beats four brilliant undated posts from an unclear year. If you already publish dashboards or visualisations, a public profile linked from your portfolio is close to free, and it is the rare credential that compounds while you sleep.

6. Sajal Sharma — When Authority Replaces the Project List

sajalsharma.com is the most instructive page in this roundup, because of what it leaves out. Its positioning line is seven words long:

I build AI systems and teach engineers how.

The sections are Writing, Teaching, About Me and Contact, plus a newsletter. There is no project gallery. The writing is current — a recent piece covers building a shared knowledge base for AI agents — and the teaching section lists live O'Reilly events on AI agents, retrieval-augmented generation and browser automation.

It is worth sitting with how completely this inverts the advice given to early-career candidates. Nobody arriving here is asking whether this person can build a churn model. The page is built for a different reader entirely: a conference organiser, a publisher, a company deciding whether to hire him to train their team. For that reader, a grid of Jupyter notebooks would actually be counter-evidence, because it would signal that the work still needs to be proved.

This is the portfolio you graduate into, and the timing matters. Move to it too early and you have a page of claims with nothing behind them. Move too late and you are a senior practitioner still leading with a Titanic dataset.

What to copy: notice the trigger rather than the layout. Once people invite you to teach or speak about your work, the invitations themselves are stronger evidence than the projects, and the portfolio should be reorganised to lead with them.

7. Chris Tran — The Minimum That Still Clears the Bar

chriskhanhtran.github.io is included deliberately as the floor, not the ceiling. It is a straightforward GitHub Pages site whose About page reads "A learner, a runner, a stargazer," and which routes visitors onward with three plain invitations: view the portfolio, read the blog posts, watch the short films.

The page states its scope honestly — Kaggle competition work, models spanning ridge and lasso regression, random forests, SVMs, XGBoost and LightGBM, then NLP applications across named-entity recognition, document classification, sentiment analysis and summarisation, plus computer vision. It reads as an early-career page, and it is stronger for not pretending otherwise.

The reason it belongs in a list of examples to study is that it is beatable, and that is encouraging rather than dismissive. Most people reading this have no portfolio at all, and their mental model of what they need to produce is something closer to entry four on this list. It isn't. A three-page site that names your methods, links your repositories and reads as a real person clears the bar you are actually being measured against, which is the bar of the candidates who sent nothing.

What to copy: the scope. Three pages — about, projects, writing — is a complete portfolio. Ship that, then improve it. For a step-by-step version of exactly this, see how to build a portfolio with no experience.

What All Seven Share — and the One Thing Almost None Do

Three habits run through every portfolio above, regardless of archetype.

• ✅ Every claim has something clickable behind it. A repository, a rendered notebook, a live demo, a paper, a published article, a scheduled course. Nothing rests on assertion alone.
• ✅ The role is stated in the first line. Not a mission statement, not a passion declaration — a plain description of what the person does, in under fifteen words.
• ✅ Projects are named in problem language, not model language. "Gender disparity in Hollywood" and "customer churn prediction," not "logistic regression notebook v2."

Now the omission, which is the genuinely interesting finding. Read any guide to data science portfolios — including the section-by-section annotated example on this site — and you will be told to lead with measurable business outcomes. Across these seven live portfolios, not one leads with a quantified business result. No conversion lifts, no cost reductions, no revenue figures in a headline position. The nearest equivalents are competitive placements and an accepted paper.

There are two honest reasons for that, and both are worth internalising. The first is legal: most commercially valuable work sits behind an NDA, so the projects a practitioner is free to publish are precisely the ones with no business metric attached. The second is that a number you cannot substantiate is worse than no number at all — an unverifiable "improved retention by 23%" invites a follow-up question you may not be able to answer in an interview.

So treat it as a floor and a ceiling. Verifiability is the floor: if a stranger cannot click through and confirm something, the item is not yet evidence. Metrics are the ceiling: when you have a number you are permitted to publish and can defend in detail, use it, because it puts you ahead of nearly everyone — including all seven portfolios here. What does not work is skipping the floor to reach for the ceiling, which is the failure mode of every portfolio that opens with bold impact claims and links to nothing. For a portfolio that reaches the ceiling precisely because it nailed the floor first, see Harsha Kumar Pasala's below.

Which Archetype Should You Copy?

Match the archetype to the doubt you need to defeat, because that is what changes as you progress.

You're switching into data from another field

Your problem is that nobody has seen you reason. Use the narrative-per-project shape: three or four projects, each with a written explanation of why you made the choices you made. Volume actively hurts here — fifteen tutorial reproductions signal that you followed instructions fifteen times. If your background is analytics-adjacent, the data analyst portfolio project templates give you a fill-in structure to start from.

You have a degree and a pile of coursework

Your problem is navigability, not quantity. Use the navigable project index: group by problem type, lead each group with your strongest entry, and be honest about what was coursework. If you came through a quantitative programme, borrow from paper-backed depth and publish one properly written case study — it will outperform six notebooks.

You're two to five years in, employed, and want better offers

Your problem is that your best work is confidential. Lean on cadence: publish small, dated, public things regularly — a monthly writeup, a visualisation series, a teardown of a public dataset. This is the stage where a portfolio on your own domain starts to matter, because recruiters search your name and you want to control what they find.

You're senior, and people already ask you to explain things

Your problem is no longer competence but attention. Move to authority-led: writing, talks and teaching first, with a short "selected work" section rather than a gallery. Keep one deep technical artifact visible so the page never reads as pure thought leadership.

You have nothing live at all

Your problem is starting. Use minimum viable and give yourself a week. Three pages, real name, methods named, two projects linked. You can move to any of the other six archetypes later, and you will do it faster from a live page than from a blank one.

Two More, Built From a Resume

Full disclosure: the two portfolios in this section were built on Seera, the tool behind this blog, and published by their owners. They are included because they answer a question the seven above cannot — what the same principles look like on a page that started as a resume rather than as a hand-built site, and at the career stage most readers here are actually at.

Harsha Kumar Pasala — every number has a file behind it

An engineering graduate working as a data analyst, with no long employment history to lean on. The portfolio carries three projects — an Excel KPI dashboard over 50,000+ supermarket transactions, a SQL analysis of 100K+ customer records, and a Power BI build tracking 10+ KPIs — and each one links straight to the artifact itself on GitHub: the workbook, the written analysis, the .pbix file.

This is the clearest illustration in the whole guide of the floor-and-ceiling idea. These projects do lead with numbers, which the seven above mostly do not. The reason the numbers work is that the floor was built first: a reader who doubts any figure is one click from the file it came from. That is the order to copy — artifact first, then the number it supports.

Devi Shree R — breadth with one unmistakable anchor

An AI and Data Science graduate, and a good example of the minimum-viable archetype handled with more range than most. Four projects span a Java student-management system, a web application, and two hardware builds — one of which, a smart active noise-cancellation helmet using DSP and bone-conduction audio, is patented. A data science internship and named certifications sit alongside them.

The patent is the lesson. Early-career portfolios usually have nothing a stranger can independently check, so anything that has been through an external process — a patent, a published paper, a competition placement, a merged pull request — belongs at the top rather than buried in a list.

Ten more data science and analytics portfolios built the same way are collected in the Seera data science showcase, all live and openable.

Getting Yours Live This Week

Every portfolio above took years to accumulate and an afternoon to publish. Those are separate problems, and conflating them is why so many strong data scientists still have no portfolio: the work exists, the page never gets built, because building it feels like a project in its own right.

It doesn't have to be. Four of these seven run on GitHub Pages, which is free and takes an afternoon if you are comfortable with a Jekyll theme and a bit of YAML. If you would rather not spend the afternoon on templating, the faster route is to start from the document that already lists your projects, methods and roles — your resume — and let a builder turn it into a structured page, then spend your actual time on the per-project writeups, which is the part no tool can do for you. That path is covered in the resume-to-portfolio guide, and if you are still weighing whether the page earns its keep, is a portfolio website worth it works through the trade-off.

One structural note whichever route you take: put it on your own domain. Six of the seven portfolios here sit on a subdomain of a hosting platform, and it costs them very little because their names are already known in their niches. Earlier in a career, the address is doing identity work — it is what a recruiter types, what appears in a search for your name, and what survives your changing tools three times.

Frequently Asked Questions

Where can I find real data science portfolio examples worth studying?

Start with practitioners who publish openly rather than with curated "best of" lists. GitHub Pages is the densest seam — the data-science-portfolio topic on GitHub surfaces hundreds of live portfolios, and the ones people actually reference rise to the top. Kaggle profiles, personal sites linked from Medium or Substack bylines, and the Featured section of LinkedIn profiles belonging to people already in your target role are the other three reliable seams. The seven portfolios in this guide were each opened and checked in September 2026, which matters more than it sounds: portfolio roundups rot quickly, and a dead link teaches you nothing at all. Two further examples in this guide — a data analyst's and an AI-and-data-science graduate's — were built on Seera and are linked in full, with ten more collected in the Seera data science showcase.

Is a GitHub profile enough, or do I need a portfolio website?

A GitHub profile on its own proves you can code and very little else. Four of the seven portfolios here are hosted on GitHub Pages, so hosting is not the dividing line — framing is. A bare repository list asks the hiring manager to work out what each project was for, which is exactly the work they are least willing to do. What the strong GitHub-hosted portfolios add is one thin layer of narrative per project: a title in plain language, a sentence on the problem, and a link to something clickable. If you would rather not hand-maintain that layer, a portfolio generated from your resume gives you the structure by default.

Should I copy one of these portfolio structures directly?

Copy the archetype that matches your career stage, not the person. An early-career analyst copying a senior practitioner's authority-led site ends up with a page full of opinions and no evidence, while a senior engineer copying an entry-level project index buries a decade of shipped work underneath coursework. The mapping in this guide is deliberately blunt about the split: a navigable project index when you have more projects than track record, narrative-per-project when you are switching fields and need to show reasoning, and an authority-led page only once your name and your writing already do the qualifying for you.

Do real data science portfolios show business impact metrics?

Mostly they do not, and that gap is the most useful finding in this roundup. Advice articles almost universally tell you to lead with measurable outcomes, yet across these seven live portfolios not one leads with a quantified business result. What they lead with instead is verifiability: a demo you can click, a notebook you can read end to end, a peer-reviewed paper, a repository with a real commit history. The honest reading is that verifiability is the floor and metrics are the ceiling. If your work sits under an NDA, a clickable artifact still clears the bar; if you do have a number you are allowed to publish, it puts you ahead of almost everyone.

How long does it take to build a data science portfolio like these?

These portfolios were accumulated over years, but the page itself is never the slow part. A GitHub Pages site on a Jekyll theme takes an afternoon, and an AI builder that reads your resume returns a structured page in about a minute. The real cost is the per-project writeup: budget two to three hours per project to write down the problem, the approach, the result, and what you would do differently. Three well-written projects beat fifteen bare repositories, so a portfolio worth actually sending is one or two weekends of writing rather than months of building.

What should a data science portfolio website template include?

Give every project the same card: the problem, the data, the method, the result, the limits and links to the code. Add a short hero and a contact line, and the portfolio reads as one body of work. The data science portfolio template breakdown lays the fields out.

Should a data science portfolio be a PDF?

As an extra, not the main page. Send one when a portal wants a file, and link back to the live site for demos. The guide to a data science portfolio PDF covers what to put in it.

Related Reading

• Product Manager Portfolio Examples: 5 Real Ones, Scored
• How to Build a Data Analyst Portfolio With No Experience
• Data Science Portfolio Example — Section-by-Section Breakdown
• Data Analyst Portfolio Example — Annotated Walkthrough
• Data Analyst Portfolio: 3 Project Templates That Get Hired
• 8 Data Analyst Portfolio Projects That Get Interviews
• How to Build a Portfolio With No Experience
• Resume to Portfolio Website: The Complete Guide
• What Is a Portfolio Website?
• Is a Portfolio Website Worth It?
• Best Portfolio Builders With Built-In Analytics
• Browse Seera Templates

Useful sources and further reading

• Natassha Selvaraj's portfolio — the narrative-per-project archetype; eight projects, each paired with a written explanation and a runnable demo
• Andrey Lukyanenko's data science portfolio — roughly forty projects grouped by problem type, with notebooks rendered through nbviewer
• Tatev Karen Aslanyan's portfolio repository — case-study papers alongside code, including the statistics work most portfolios omit
• James Le's site — the multi-track structure, with data and coding portfolios kept as separate sections
• Yu Dong's personal website — a weekly visualisation series running since 2018, the clearest example of cadence as evidence
• Sajal Sharma's site — the authority-led archetype: writing and teaching in place of a project gallery
• Chris Tran's portfolio — the minimum-viable GitHub Pages structure, useful as a realistic starting target
• GitHub: the data-science-portfolio topic — the best single seam for finding more live portfolios in your own specialism
• GitHub Docs: About GitHub Pages — the free hosting route four of these seven portfolios use
• Seera: Resume to Website — the faster route referenced above; it reads your CV and returns a structured portfolio page on a real domain in about a minute