A data science portfolio with 3–5 end-to-end projects outperforms a resume listing certifications and Kaggle rankings. Each project needs: a real-world dataset (not Titanic or Iris), a clear problem statement with business framing, data cleaning and feature engineering, model selection with evaluation metrics, and a clean GitHub README explaining the "so what." The strongest portfolios include at least one deployed model (Streamlit or Gradio demo) and show the full pipeline from raw data to production-ready output.
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How many portfolio projects does a data scientist need?
3–5 projects is the sweet spot. Fewer than 3 doesn't demonstrate range across the data science workflow. More than 7 dilutes quality and signals quantity-over-depth thinking. The ideal portfolio: one strong EDA/visualization project, two ML projects showing different problem types (classification, NLP, time series), and one end-to-end deployed project. Quality and depth beat volume every time.
What datasets should data scientists use for portfolio projects?
Use real-world, messy datasets from the UCI ML Repository, government open data (Census, CDC, CMS), completed Kaggle competitions, or self-scraped data. Avoid tutorial defaults like Titanic, Iris, Boston Housing, and MNIST — hiring managers have reviewed them hundreds of times. The messier and more realistic the data, the better — data cleaning and feature engineering are 60–80% of real data science work, and skipping them signals inexperience.
Where should data scientists host their portfolio?
GitHub is the primary portfolio platform — clean repositories with documented notebooks, requirements files, and professional READMEs. Deploy at least one model demo on Streamlit Cloud or Hugging Face Spaces. Write blog posts on Medium, Substack, or a personal site to explain methodology. The minimum viable portfolio: a GitHub profile with 3–5 repositories, one deployed demo, and one technical write-up explaining a project's methodology and results.
A hiring manager reviewing data scientist applications doesn't care how many Coursera certificates are on the resume. They care whether the candidate can take a messy dataset, frame a business problem, build a model, evaluate it honestly, and explain what it means. The only way to prove that is to show the work — and the only place to show the work is a portfolio.
But most portfolio advice stops at "do a Kaggle competition." Which competition? With what framing? Demonstrating what skills? This guide provides 15 specific project ideas — with datasets, tools, difficulty levels, and time estimates — that demonstrate the skills hiring managers actually evaluate.
Certifications prove course completion. Portfolios prove competence. For data science candidates — especially career changers and self-taught practitioners — this distinction determines who gets interviews.
A data science portfolio does three things a resume and certificates cannot:
- Demonstrates end-to-end thinking — not just "knows scikit-learn" but how the entire pipeline works from data acquisition to model evaluation
- Shows communication ability — a well-written README and notebook narrative proves the ability to explain statistical decisions to non-technical stakeholders
- Proves depth over breadth — a single well-executed project with proper cross-validation, feature engineering, and honest error analysis signals more competence than 10 tutorial completions
For the complete path from foundations to job offer — including skills, education options, and job search strategy — see How to Become a Data Scientist in 2026.
A data science portfolio with 3–5 end-to-end projects and honest model evaluation outperforms a resume listing certifications. Portfolios prove what credentials cannot: the ability to move from a raw dataset to a business-relevant insight using rigorous methodology.
Not all projects demonstrate equal competence. Understanding what hiring managers evaluate separates portfolios that generate interviews from ones that get skipped.
Every data science portfolio project needs five elements: a real dataset, a clear problem statement, documented feature engineering, rigorous model evaluation with multiple metrics, and a professional README. The README is as important as the model — it's what hiring managers read first and often the only thing they read.
Here are 15 specific projects — organized by difficulty — that demonstrate the skills hiring managers evaluate.
These projects demonstrate foundational data science skills: exploratory data analysis, basic modeling, visualization, and the ability to frame analysis around a question. Complete 2–3 before moving to intermediate projects.
Customer Churn Prediction (Classification)
Problem statement: Which customers are most likely to cancel their subscription, and what factors drive churn?
Dataset: Telco Customer Churn (Kaggle) — 7,043 customers with 21 features including tenure, contract type, and monthly charges.
Skills demonstrated: EDA, feature encoding, logistic regression, decision trees, classification metrics Difficulty: Beginner | Time estimate: 1–2 weeks
Deliverables:
- Exploratory analysis with visualizations of churn drivers
- Logistic regression and decision tree models with comparison
- Evaluation using precision, recall, F1-score, and AUC-ROC
- Written summary with 3 retention recommendations based on model insights
Housing Price Prediction (Regression)
Problem statement: What features most influence home sale prices, and how accurately can prices be predicted?
Dataset: Ames Housing Dataset (Kaggle) — 2,930 observations with 80 features. Far richer than the deprecated Boston Housing dataset.
Skills demonstrated: Regression modeling, feature selection, handling missing data, residual analysis Difficulty: Beginner | Time estimate: 1–2 weeks
Deliverables:
- Feature importance analysis with correlation heatmaps
- Linear regression and random forest models with RMSE comparison
- Residual plots showing model assumptions and limitations
- Clear explanation of which features a homeowner could change to increase value
Exploratory Data Analysis: Global Health Indicators
Problem statement: How do health spending, GDP, and life expectancy relate across countries, and which nations outperform their economic peers?
Dataset: WHO Global Health Observatory — health expenditure, life expectancy, and mortality indicators by country and year.
Skills demonstrated: Data cleaning across multiple sources, pandas proficiency, statistical visualization, storytelling with data Difficulty: Beginner | Time estimate: 1–2 weeks
Deliverables:
- Cleaned, merged dataset from multiple WHO tables
- 8–10 publication-quality visualizations (matplotlib/seaborn)
- Statistical analysis of spending-to-outcome efficiency by region
- Narrative notebook with markdown explanations of each finding
Credit Card Fraud Detection (Imbalanced Classification)
Problem statement: Can fraudulent transactions be detected with high recall while minimizing false positives?
Dataset: Credit Card Fraud Detection (Kaggle) — 284,807 transactions with only 492 frauds (0.17% positive class).
Skills demonstrated: Handling class imbalance (SMOTE, undersampling), precision-recall trade-offs, threshold tuning Difficulty: Beginner–Intermediate | Time estimate: 1–2 weeks
Deliverables:
- Comparison of sampling strategies (SMOTE, random undersampling, class weights)
- Precision-recall curves with business-informed threshold selection
- Cost-benefit analysis of false positive vs. false negative rates
- Model selection justified by the business context (recall matters more than accuracy here)
Spotify Listening Patterns Analysis
Problem statement: What audio features predict song popularity, and how have musical trends shifted over time?
Dataset: Spotify Tracks Dataset (Kaggle) — 114K+ tracks with audio features like danceability, energy, and tempo.
Skills demonstrated: Feature exploration, correlation analysis, time-series trends, data visualization Difficulty: Beginner | Time estimate: 1 week
Deliverables:
- Audio feature distributions and correlation analysis
- Trend analysis of genre and feature evolution over decades
- Predictive model for track popularity based on audio features
- Interactive visualizations showing feature clusters across genres
Beginner projects demonstrate Python fundamentals, basic ML modeling (classification and regression), and the ability to clean data and communicate findings. Complete 2–3 beginner projects before moving to intermediate — they prove baseline competence and form the foundation of the portfolio.
Intermediate projects raise the bar: specialized ML techniques, real-world complexity, and projects closer to actual production data science work.
These projects demonstrate deeper technical skill: NLP, time series, recommendation systems, and experimental analysis. They're the projects that actually differentiate candidates.
NLP Sentiment Analysis on Product Reviews
Problem statement: Can customer sentiment be accurately classified from review text, and what themes drive negative reviews?
Dataset: Amazon Product Reviews (Kaggle) — 500K+ food reviews with ratings, text, and metadata.
Skills demonstrated: Text preprocessing (tokenization, TF-IDF), NLP modeling, topic extraction Difficulty: Intermediate | Time estimate: 2–3 weeks
Deliverables:
- Text preprocessing pipeline (cleaning, tokenization, vectorization)
- Sentiment classification using TF-IDF + logistic regression and a transformer-based approach
- Topic modeling (LDA) to extract themes from negative reviews
- Business recommendations based on recurring complaint patterns
Time Series Forecasting: Energy Demand
Problem statement: How accurately can next-day electricity demand be predicted, and what drives forecast errors?
Dataset: PJM Hourly Energy Consumption (Kaggle) — hourly energy consumption data across multiple US regions over 10+ years.
Skills demonstrated: Time series decomposition, ARIMA/Prophet, feature engineering with lag variables, forecast evaluation Difficulty: Intermediate | Time estimate: 2–3 weeks
Deliverables:
- Time series decomposition showing trend, seasonality, and residuals
- Comparison of ARIMA, Prophet, and gradient boosting approaches
- Feature engineering with lag features, rolling averages, and calendar effects
- Forecast accuracy evaluation using MAE, RMSE, and MAPE with honest error analysis
Movie Recommendation Engine
Problem statement: Can user preferences be predicted to recommend films they'll rate highly?
Dataset: MovieLens 25M (GroupLens) — 25M ratings from 162K users on 62K movies with tag data.
Skills demonstrated: Collaborative filtering, matrix factorization, cold-start handling, evaluation with ranking metrics Difficulty: Intermediate | Time estimate: 2–3 weeks
Deliverables:
- Collaborative filtering (user-based and item-based) implementation
- Matrix factorization using SVD or ALS
- Evaluation using RMSE, precision@k, and NDCG
- Cold-start strategy for new users and new movies
A/B Test Analysis with Statistical Rigor
Problem statement: Did a website redesign improve conversion rates, and how confident should the business be in the result?
Dataset: Kaggle A/B Testing Datasets or a synthetic dataset designed with realistic conversion rates and sample sizes.
Skills demonstrated: Hypothesis testing, confidence intervals, power analysis, effect size estimation Difficulty: Intermediate | Time estimate: 2 weeks
Deliverables:
- Frequentist and Bayesian approaches to the same test
- Sample size calculation and power analysis
- Visualization of conversion funnels and confidence intervals
- Executive summary explaining results in non-technical language with a clear recommendation
Image Classification with Transfer Learning
Problem statement: Can plant diseases be identified from leaf images using deep learning?
Dataset: PlantVillage Dataset (Kaggle) — 54K+ images of healthy and diseased plant leaves across 38 classes.
Skills demonstrated: CNNs, transfer learning (ResNet/EfficientNet), data augmentation, GPU training Difficulty: Intermediate | Time estimate: 2–3 weeks
Deliverables:
- Baseline CNN trained from scratch vs. transfer learning comparison
- Data augmentation strategy with before/after performance metrics
- Confusion matrix showing per-class performance
- Grad-CAM visualizations showing what the model "sees" in each image
Every portfolio project should translate into a resume bullet with quantified impact. For the exact formula, see Data Scientist Resume Guide.
Intermediate projects demonstrate the ability to apply specialized ML techniques — NLP, time series, recommendation systems, statistical experimentation, and deep learning — to realistic problems. These projects carry the most weight in hiring evaluations because they're closest to actual production data science work.
Advanced projects separate senior candidates from mid-level ones: deployment, LLMs, causal reasoning, and production-grade engineering.
These projects signal production readiness: model deployment, LLM applications, causal inference, and open-source contributions. Include one or two to stand out in competitive applicant pools.
End-to-End ML Pipeline with Deployment
Problem statement: Build a complete ML system from data ingestion to live prediction API — the kind of thing a production data science team actually ships.
Dataset: Any structured prediction problem — real estate pricing, customer churn, or demand forecasting with live data.
Skills demonstrated: MLOps, Docker, API development, model versioning, CI/CD for ML Difficulty: Advanced | Time estimate: 3–4 weeks
Deliverables:
- Feature engineering pipeline in Python (pandas/Polars)
- Trained model with experiment tracking (MLflow or Weights & Biases)
- FastAPI prediction endpoint with input validation
- Docker container deployable to any cloud provider
- Streamlit or Gradio frontend for interactive demo
- README documenting the full architecture and deployment steps
LLM-Powered Application (RAG System)
Problem statement: Build a retrieval-augmented generation (RAG) system that answers questions about a specialized knowledge base with cited sources.
Dataset: A domain-specific corpus — company documentation, academic papers, legal texts, or medical guidelines.
Skills demonstrated: LLM integration, embedding models, vector databases, prompt engineering, evaluation of generated outputs Difficulty: Advanced | Time estimate: 3–4 weeks
Deliverables:
- Document chunking and embedding pipeline (OpenAI/Sentence Transformers)
- Vector store implementation (ChromaDB, Pinecone, or FAISS)
- RAG pipeline with source citation and retrieval evaluation
- Evaluation framework measuring answer relevance, faithfulness, and hallucination rate
- Deployed demo on Streamlit or Gradio with sample queries
Causal Inference Study
Problem statement: Does a specific intervention (policy change, marketing campaign, product feature) actually cause an outcome, or is the correlation spurious?
Dataset: Observational data with a natural experiment — state-level policy changes, platform feature rollouts, or economic shocks. LaLonde Dataset for job training program evaluation is a classic starting point.
Skills demonstrated: Causal reasoning, propensity score matching, difference-in-differences, regression discontinuity Difficulty: Advanced | Time estimate: 3–4 weeks
Deliverables:
- Causal framework diagram (DAG) explaining assumptions
- Propensity score matching or instrumental variable analysis
- Comparison of naive correlation vs. causal estimate
- Sensitivity analysis testing robustness of conclusions
- Written report explaining causal reasoning for a non-technical audience
Production ML Monitoring Dashboard
Problem statement: Build a system that detects model drift, data quality issues, and performance degradation in a deployed ML model.
Dataset: Simulated production data with injected drift — gradually shifting feature distributions and label frequencies over time.
Skills demonstrated: Model monitoring, data drift detection, alerting systems, MLOps Difficulty: Advanced | Time estimate: 2–3 weeks
Deliverables:
- Data drift detection using statistical tests (KS test, PSI)
- Performance monitoring tracking key metrics over time
- Alerting logic with configurable thresholds
- Dashboard (Streamlit or Grafana) visualizing drift and performance trends
- Documentation covering when and how to retrain
Open-Source Contribution
Problem statement: Contribute meaningful code, documentation, or benchmarks to an established open-source ML library.
Target projects: scikit-learn, Hugging Face Transformers, pandas, XGBoost, or any ML library with open issues labeled "good first issue."
Skills demonstrated: Collaboration, code quality, testing, documentation, working within established codebases Difficulty: Advanced | Time estimate: Varies (2–6 weeks)
Deliverables:
- Merged pull request (or well-documented open PR) to a recognized project
- Blog post explaining the contribution — what problem it solves, the implementation approach, and what was learned
- Evidence of code review participation and community interaction
Not sure which technical skills to prioritize for your projects? See the complete breakdown in Data Science Skills: What Employers Actually Want in 2026.
Advanced projects demonstrate what separates senior data scientists from mid-level: the ability to deploy models to production, work with cutting-edge tools like LLMs and vector databases, apply causal reasoning beyond correlation, and contribute to the broader ML community. One or two advanced projects in a portfolio signals readiness for senior or staff-level roles.
Projects are only valuable if hiring managers can find and evaluate them quickly. Presentation determines whether a strong project gets attention or gets skipped.
# [Project Title]
Problem Statement
[One sentence: what business or research question does this project answer?]
Dataset
Approach
1. Data Cleaning & EDA — [Summary of preprocessing steps]
2. Feature Engineering — [Key transformations and rationale]
3. Modeling — [Algorithms used and why]
4. Evaluation — [Metrics, cross-validation strategy]
Key Results
| Metric | Score |
|--------|-------|
| AUC-ROC | 0.XX |
| Precision | 0.XX |
| Recall | 0.XX |
Key Findings
1. [Most important finding with specific number]
2. [Second finding]
3. [Business implication or recommendation]
How to Run
```bash
pip install -r requirements.txt
python src/train.py
streamlit run app.py # for demo
```
Files
Methodology & Limitations
[2-3 paragraphs on approach, assumptions, and what the model does NOT capture]
Portfolio presentation essentials:
- GitHub structure matters. Every project gets its own repository with a clean README, a
requirements.txt, and organized folders (data/,notebooks/,src/). - Deploy at least one model. A Streamlit or Gradio app on Streamlit Cloud or Hugging Face Spaces turns a static notebook into an interactive demo a recruiter can click.
- Write one technical blog post. Explaining methodology in plain language — why a particular model was chosen, what trade-offs were made — proves communication skills that notebooks alone cannot.
- GitHub profile README. Create a profile-level README that links to all portfolio projects with one-line descriptions and links to live demos.
A portfolio is one step in the data science career journey. For the complete roadmap from beginner to hired, including education paths and skill prioritization, see Data Scientist Career Path.
- 013–5 end-to-end portfolio projects beat a resume full of certifications — portfolios prove competence, credentials prove course completion
- 02Use real-world, messy datasets from UCI ML Repository, government open data, or completed Kaggle competitions — avoid Titanic, Iris, and MNIST as primary projects
- 03Every project needs: a problem statement, documented data cleaning and feature engineering, model selection rationale, evaluation with multiple metrics, and a professional README
- 04Beginner projects demonstrate EDA and basic modeling; intermediate projects show specialized ML skills (NLP, time series, recommendations); advanced projects signal production readiness (deployment, LLMs, causal inference)
- 05Deploy at least one model with Streamlit or Gradio — interactive demos make portfolios memorable and prove engineering capability beyond notebook analysis
- 06The README is the most important file in each project — hiring managers read it first, and a well-documented project with average results beats a brilliant model with no explanation
Can I use Kaggle datasets for my data science portfolio?
Yes — but avoid the most overused tutorial datasets (Titanic, Iris, MNIST, Boston Housing). Use datasets from completed Kaggle competitions, the Kaggle Datasets section, or combine Kaggle data with other sources. The key: choose datasets messy enough to require real data cleaning and complex enough to support feature engineering. Bonus points for datasets not commonly used in tutorials — it signals independent thinking.
How long should each data science portfolio project take?
Beginner projects: 1–2 weeks. Intermediate projects: 2–3 weeks. Advanced projects: 3–4 weeks. These timelines assume 10–15 hours per week of focused work. The most common mistake is spending months perfecting one project instead of building a portfolio that shows range across problem types and techniques.
Do I need a deployed model in my portfolio?
For mid-level and senior roles, yes — at least one deployed demo is strongly recommended. Streamlit Cloud and Hugging Face Spaces offer free hosting. A deployed model proves engineering skills beyond notebook analysis: API design, input validation, error handling, and user interface design. For entry-level roles, a clean notebook with strong methodology is sufficient, but a deployed demo is a significant differentiator.
Should I include Kaggle competition rankings in my portfolio?
Kaggle medals and rankings add credibility but shouldn't be the entire portfolio. Competition code is often optimized for leaderboard position rather than readability, reproducibility, or business relevance. If you have a strong Kaggle ranking, showcase it — but pair it with at least 2–3 projects that demonstrate end-to-end data science work with business framing and clean documentation.
What if my portfolio projects use different tools than the job posting requires?
The methodology transfers across tools. A strong scikit-learn project demonstrates ML fundamentals that apply to any framework. A well-structured pandas pipeline proves data engineering skills regardless of whether the company uses Spark. Focus on demonstrating statistical rigor, clean engineering practices, and business thinking — framework-specific syntax is the easiest skill to learn on the job.
Prepared by Careery Team
Researching Job Market & Building AI Tools for careerists · since December 2020
- 01Build a Career in Data Science — Emily Robinson & Jacqueline Nolis (2020)
- 02Occupational Outlook Handbook: Data Scientists — Bureau of Labor Statistics (2025)
- 03Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter — Wes McKinney (2022)