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The strongest data science portfolio is not a stack of machine-learning demos. Choose projects that fit your target role, answer a concrete question, and end in an artifact a stranger can understand: a notebook, dashboard, app, or concise recommendation. The 20 ideas below span analysis, visualization, prediction, text, and data engineering; none is ranked by hiring outcomes.
What makes a good data portfolio project?
A useful project shows more than code. It makes the question clear, identifies where the data came from, explains preparation choices, and presents results with appropriate caveats. When modeling is involved, include a baseline and evaluation that matches the task; for example, use precision and recall for a churn classifier or backtesting against a naive forecast for sales prediction. Finish with a readable deliverable and an explanation of what a stakeholder could reasonably do with the result.
Projects should also match the work you want to do. Analyst-oriented work can emphasize cleaning, exploratory analysis, and dashboards; data-scientist work can demonstrate modeling and evaluation; engineering work can show reliable ingestion and validation. As Anishta Purrahoo writes in Dataquest’s guide to data science projects, “Many strong portfolio projects don’t involve ML at all.”
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20 data science project ideas, grouped by the work they demonstrate
Analysis, cleaning, and visualization
- Analyze a data-science survey. Compare reported tools or skills by role or experience level, then write a short report that explains response and sampling caveats. Dataquest’s example uses Kaggle survey data.
- Assess an app-market opportunity. Clean app-store data and compare category prevalence with installs or ratings as proxies. End with a qualified product recommendation rather than treating popularity measures as proof of demand.
- Explore a public-health time series. Visualize COVID-19 or another public-health trend, carefully explaining the limits of comparisons across regions and time. Dataquest’s example points to Our World in Data.
- Analyze Hacker News discussions. Explore post timing, titles, and engagement, demonstrating string and date handling. Describe how the sample was collected so readers can judge its coverage.
- Clean used-car listings. Address missing values, inconsistent records, and outliers; show how those decisions affect price summaries.
- Tell an exchange-rate story. Build an annotated time-series visualization with clear dates and scales, and explain what the observed changes do and do not establish.
- Study traffic patterns. Identify conditions associated with heavy traffic and present practical limits on interpreting those associations as causal.
- Analyze an e-commerce funnel. Use event data to identify where users drop out, then propose one measurable experiment. D8A’s example uses a GA4 sample dataset in BigQuery.
- Analyze a local Airbnb market. Clean listing data for one city and surface patterns that may help hosts, while explaining coverage and selection limitations.
- Build a one-page business KPI dashboard. Choose a small, relevant set of KPIs, add a useful filter, and include a stakeholder-oriented explanation, written or recorded.
Prediction, forecasting, and segmentation
- Predict house prices. Establish a simple baseline, document feature choices, and evaluate error on held-out data.
- Predict customer churn. Compare precision and recall, show a confusion matrix, and explain how a business might use or misuse the predictions.
- Forecast store sales. Backtest over time, compare the model with a naive baseline, and report forecast error rather than relying on a single split.
- Segment credit-card customers. Explore whether clusters are stable and interpretable before suggesting possible uses.
- Predict insurance costs. Explain how the target is constructed, consider potentially sensitive variables, and compare errors across relevant groups where the data permits.
- Classify heart-disease records. Use a public dataset carefully, report evaluation and limitations, and make clear that a portfolio model is not a clinical tool.
Text, apps, and data engineering
- Classify product-review sentiment. Compare a simple TF-IDF baseline with a more advanced text model, then inspect examples where each approach fails.
- Classify spam or misinformation text. Define labels and the costs of false positives and false negatives; explain why automated labels should not be treated as ground truth. Dataquest lists spam filtering and fake-news detection examples.
- Build a document question-answering app. Create a retrieval-based demo over a small document set, show source references, and document cases where answers fail. D8A includes a retrieval-augmented generation example.
- Build a public-API data pipeline. Fetch data on a schedule, validate it, load it into storage, and document logging, retries, and one handled failure.
How to choose an idea you can finish
Use these questions to narrow the list before choosing tools or adding complexity:
#1 Best Overall
- Role fit: Does the work demonstrate skills relevant to the analyst, scientist, engineering, or business role you are targeting?
- Question and audience: Can you state one specific question and identify who could use the answer?
- Data provenance and messiness: Can you explain the source, its limitations, and the preparation or validation the project requires?
- Method and evaluation: Is there an appropriate baseline, and does the evaluation fit the task, such as backtesting for forecasts or classification trade-offs for churn?
- Deliverable and reproducibility: Can another person open the notebook, dashboard, or app and understand the problem, approach, result, and limitations?
- Scope: Can you complete the work with your current skills and available time? Dataquest’s March 27, 2026 guide gives rough planning estimates of one to two weeks for beginner projects and two to four weeks for intermediate or advanced projects; these are estimates, not guarantees.
Prefer a focused version with a clear ending over an ambitious project that never becomes reviewable. For a survey analysis, for example, a documented comparison across a few roles may be more useful than an unfinished attempt to model every response.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How many projects should be in a data portfolio?
There is no hiring threshold established by these guides. Dataquest recommends three to five finished, well-documented projects; D8A Academy recommends three or four projects that show range, including at least one end-to-end piece. Treat both as editorial guidance, not measured rules about employer decisions. A compact portfolio can cover complementary skills—for example, a cleaning and analysis project, a well-evaluated prediction or forecast, and an end-to-end dashboard or data pipeline.
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Rank #2
What to include when you publish a project
- Purpose: State the question, intended audience, and why the result might matter.
- Data: Identify the source and describe its coverage, collection, and meaningful limitations. Check dataset terms and availability before reproducing an example.
- Approach: Explain preparation decisions, the baseline, methods, and evaluation choices in plain language as well as code.
- Result: Present the key finding or model behavior in a readable format and avoid claims stronger than the data supports.
- Limitations and next action: Note uncertainty, likely failure modes, and a reasonable follow-up, such as a measured experiment or additional validation.
- Reproducibility: Make the artifact easy to open and follow, with enough context for a reviewer who has not seen the project before.
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