Hands-on projects are the single most powerful way to learn data analytics and get noticed by recruiters in 2026. Projects force you to apply SQL, Python, Excel, and BI tools to real problems, build storytelling instincts, and create tangible portfolio pieces you can link on your resume or GitHub. This guide lists 20 project ideas from beginner to advanced, explains what each project should include, suggests datasets and tools, and highlights the skills and outcomes recruiters care about. Use these projects to build a portfolio that demonstrates technical competence, business impact, reproducibility, and clean presentation.
Why Data Analyst Projects Matter for Getting Hired in 2026
Projects demonstrate practical ability that interviews and resumes alone cannot prove. Recruiters look for measurable outcomes, clear visualizations, reproducible code, and domain knowledge. A strong project shows you can translate messy data into business insights, design intuitive dashboards, and explain decisions with metrics. Projects also provide talking points for interviews and deliverables employers can evaluate.
What Recruiters Look for in a Data Analyst Portfolio
Recruiters evaluate storytelling, clarity, and impact. They expect a concise README, a short case summary that states business question, data sources, approach, and outcomes, clean dashboards with narrative captions, reproducible code or notebooks, and a clear description of how the project moved metrics or informed decisions.
Tools You’ll Use Across These Projects
Most projects make heavy use of these tools: Excel for quick analysis and pivoting, SQL for data extraction, Python for EDA and modeling, Power BI or Tableau for dashboarding, and GitHub to host code and documentation. Include keywords like data analyst projects 2026, SQL projects, Power BI projects, and forecasting models in your project descriptions to improve discoverability.
Beginner Data Analyst Projects (2026)
Projects in this section require simple datasets, spreadsheet work, and basic SQL. Each is designed to be completed in a weekend to two weeks with clear deliverables for a portfolio.
- Sales Data Dashboard (Excel / Power BI)
Goal: Build a sales performance dashboard that tracks revenue, units sold, top products, and regional performance. Include KPIs such as month over month growth and average order value.
What to include: raw dataset, cleaned file, pivot tables or SQL queries, a Power BI dashboard with filters and slicers, and a one page insights summary.
Datasets: sample ecommerce sales CSV or Kaggle retail sales datasets.
Skills gained: Excel pivoting, Power BI visuals, KPI design, business storytelling.
- Customer Segmentation Using Excel / Python
Goal: Segment customers by spending, frequency, and recency to identify high value and churn risk segments.
What to include: RFM calculations in Excel or Python, clustering using k-means in Python, segment descriptions, and actionable recommendations.
Datasets: retail transaction datasets, customer purchase logs.
Skills gained: customer analytics, clustering basics, feature engineering, segment targeting.
- Movie Ratings Analytics (Kaggle Dataset)
Goal: Clean a movie ratings dataset and present EDA that uncovers trends in ratings by genre, year, and user activity.
What to include: data cleaning steps, visual explorations, top findings, and small dashboard or interactive notebook.
Datasets: MovieLens or Kaggle movie datasets.
Skills gained: EDA, data cleaning, visualization best practices.
- HR Attrition Dashboard
Goal: Analyze employee turnover drivers and present a dashboard to HR with filters for department, tenure, and attrition reason.
What to include: data preprocessing, attrition rate calculations, key drivers analysis, Power BI dashboard, and short recommendations to reduce turnover.
Datasets: synthetic HR datasets or IBM HR Analytics dataset.
Skills gained: HR analytics, KPI construction, dashboard design, stakeholder-focused insights.
- Basic SQL Querying Project
Goal: Write a set of SQL queries for a small ecommerce schema to answer business questions such as top customers, monthly revenue, and product returns.
What to include: schema diagram, sample queries (SELECT, JOIN, GROUP BY, window functions), and a small report documenting results.
Datasets: ecommerce orders and customers tables or public schemas.
Skills gained: SQL fundamentals, joins, aggregation, window functions, query optimization.
Intermediate Data Analyst Projects (2026)
Intermediate projects add Python, advanced EDA, modeling basics, and richer BI dashboards. Expect to spend two to six weeks per project.
- E-commerce Sales Performance Dashboard (Power BI / Tableau)
Goal: Build an interactive dashboard to track category performance, identify rising products, and support merchandising decisions.
What to include: data model, calculated measures, drill-through pages, and executive summary with recommended merchandising actions.
Datasets: ecommerce sales logs, product catalog.
Skills gained: dashboard UX, DAX or calculated fields, time intelligence, stakeholder-ready reporting.
- COVID-19 Data Tracker
Goal: Build a time-series tracker using public APIs that visualizes cases, vaccinations, and regional trends with forecasting.
What to include: API ingestion pipeline, cleaned time-series, visual trend analysis, short-term forecasting, and an explanation of data limitations.
Datasets: public health APIs and repositories.
Skills gained: API data pipelines, time-series visualization, smoothing, data ethics and provenance.
- A/B Testing Case Study
Goal: Design and analyze a controlled experiment comparing two variants for a product feature, focusing on statistical validity and business significance.
What to include: hypothesis, sample size calculation, test execution plan, statistical tests, and interpretation with confidence intervals.
Datasets: simulated experiment logs or product analytics exports.
Skills gained: experimental design, statistical testing, p values and effect sizes, practical interpretation.
- Financial Market Analysis (Python + Yahoo Finance API)
Goal: Analyze stock trends, compute moving averages, volatility, and visualize trading patterns to create a simple trading dashboard.
What to include: data ingestion from APIs, feature engineering, candlestick charts, and a notebook with trading insights.
Datasets: historical stock price data via public APIs.
Skills gained: time-series handling, financial indicators, visualization, risk interpretation.
- Retail Inventory Optimization Project
Goal: Identify products at risk of stockouts and propose reorder points using sales history and lead time.
What to include: demand estimation, safety stock calculation, recommendations for reorder policies, and simple simulation.
Datasets: sales by SKU and supplier lead times.
Skills gained: inventory analytics, forecasting basics, operational metrics.
- Loan Approval Analysis
Goal: Analyze a loan dataset to identify factors associated with approval rates and build a dashboard for credit officers.
What to include: data cleaning, missing value strategy, approval rate tables, and a dashboard showing risk indicators and actionable flags.
Datasets: public lending datasets, UCI credit data.
Skills gained: categorical variable handling, logistic interpretation, stakeholder reporting.
- Marketing Campaign Effectiveness Analysis
Goal: Measure campaign ROI across channels and recommend budget allocation based on conversion effectiveness.
What to include: attribution modeling basics, conversion funnel analysis, and a campaign ROI dashboard.
Datasets: marketing performance datasets, ad spend and conversions.
Skills gained: attribution concepts, cohort analysis, ROI calculation, cross-channel comparison.
Advanced Data Analyst Projects (2026)
Advanced projects are suitable for senior portfolios or capstones. They often involve forecasting, machine learning classification, anomaly detection, or full BI pipelines. Plan for several weeks to months for robust implementations.
- Revenue Forecasting Model (Time-Series)
Goal: Build and compare forecasting models such as ARIMA, Prophet, and simple machine learning regressors to predict revenue and quantify forecast uncertainty.
What to include: data preprocessing, seasonality decomposition, model comparisons, forecast accuracy metrics, and a dashboard showing forecast vs actual.
Datasets: historical sales or revenue series.
Skills gained: advanced time-series modeling, model selection, forecast communication.
- Customer Churn Prediction (Analytics + ML)
Goal: Predict which customers are likely to churn using classification algorithms and build a retention playbook.
What to include: feature engineering, model training and validation, lift charts, precision-recall analysis, and an operational recommendation document.
Datasets: SaaS or subscription datasets with churn labels.
Skills gained: predictive modeling, evaluation metrics for imbalanced data, business translation of models.
- Supply Chain Analytics Dashboard
Goal: Present end-to-end supply chain KPIs including lead time, fill rate, supplier performance, and transportation cost breakdowns.
What to include: ETL pipeline for multiple data sources, normalized schema, interactive dashboard, and scenario analysis for cost vs lead time tradeoffs.
Datasets: supply chain logs, supplier invoices, shipment manifests.
Skills gained: multi-source integration, ETL design, supply chain metrics, scenario modeling.
- Healthcare Patient Analytics Project
Goal: Analyze patient readmissions and build a dashboard for clinicians and administrators that highlights risk factors and resource utilization.
What to include: data governance considerations, privacy preserving steps, readmission risk analysis, and operational recommendations.
Datasets: synthetic healthcare datasets or deidentified public datasets.
Skills gained: domain knowledge, privacy awareness, risk modeling, stakeholder communication.
- Fraud Detection Analytics
Goal: Detect anomalous transactions using statistical and machine learning approaches and present a monitoring dashboard for operations teams.
What to include: unsupervised anomaly detection, feature engineering for transaction behavior, alerting thresholds, and a cost-benefit analysis.
Datasets: transaction logs with labels if available.
Skills gained: anomaly detection, unsupervised learning, false positive management, operationalization.
- Real Estate Price Analysis
Goal: Build region-level price trend analysis and local segmentation to advise investors or homebuyers.
What to include: EDA, hedonic regression or tree-based models for price drivers, interactive map visualizations, and neighborhood insights.
Datasets: real estate listings, historical sale prices, census features.
Skills gained: geospatial analysis, regression interpretation, market segmentation.
- Airline Delay Prediction Analysis
Goal: Use flight data to identify delay drivers and create a predictive indicator for on-time performance.
What to include: feature extraction from schedules and weather, EDA of delay patterns, predictive modeling, and an operations dashboard.
Datasets: public flight delay datasets.
Skills gained: feature engineering from diverse sources, temporal patterns, performance metrics for classification/regression.
- End-to-End BI Portfolio Project (Full Pipeline)
Goal: Build a professional capstone that demonstrates the full analytics lifecycle from data ingestion to production dashboards and insights report.
What to include: data cleaning scripts, SQL database schema and queries, Python notebooks for EDA, a Power BI or Tableau dashboard, deployment notes, README with business context and KPI impact, and a short video walkthrough or slide deck.
Datasets: choose a domain you want to specialize in such as finance, ecommerce, healthcare, or logistics.
Skills gained: end-to-end ownership, reproducibility, production-ready dashboards, cross-functional communication.
How to Showcase These Projects in Your Resume and GitHub
To make your data analyst projects portfolio-ready, follow a clear, consistent structure.
On GitHub
Include a concise README with:
- Project summary
- Business problem and approach
- Main insights
- Technologies used (SQL, Python, Power BI, etc.)
- Dataset link and dashboard screenshots
- Optional: Live dashboard/demo links for Power BI or Tableau projects
Ensure clean code, organized folders, and minimal clutter.
On Your Resume
List 2–3 bullet points per project. Focus on impact and metrics:
- “Built a churn prediction model using Python; improved accuracy by 15%”
- “Created a Power BI dashboard tracking $5M+ in sales across regions”
Skills You Gain After Completing These 20 Projects
By completing these projects, you build job-ready skills across key tools and domains:
- SQL: Complex queries, joins, aggregations
- Python: Data cleaning, EDA, basic ML, automation
- Excel: Advanced formulas, pivot tables, scenario analysis
- Power BI/Tableau: Interactive dashboards, KPI tracking
- EDA & Forecasting: Time-series analysis, churn models
- Domain Knowledge: Finance, retail, healthcare, marketing analytics
FAQs — Data Analyst Projects 2026
- Which data analyst project is best for beginners in 2026?
A sales data dashboard or basic SQL querying project is best. They require minimal tooling, teach core concepts, and produce clear portfolio deliverables.
- Do recruiters check project GitHub repositories?
Yes. Recruiters and hiring managers often look at GitHub to verify coding ability, documentation quality, and reproducibility. A well documented repository improves credibility.
- How many projects should I include in my portfolio?
Quality over quantity matters. Include three to six polished projects that cover different skills such as SQL, visualization, and a predictive model or end-to-end pipeline.
- Can I get a job with just portfolio projects?
Yes, if your projects show practical impact, good communication, and reproducible work. Combine projects with interview preparation, networking, and role-specific practice.
- What tools do I need to build these data analyst projects?
At minimum you will use Excel, SQL, Python, and one BI tool such as Power BI or Tableau. For advanced projects add libraries such as pandas, scikit-learn, Prophet, and cloud or deployment tools.
- Which domain projects are most valued in analytics jobs?
Finance, ecommerce, healthcare, and SaaS analytics projects are highly valued. Choose a domain aligned with the industry you want to work in and show domain-specific metrics.