Last updated: October 2026. Written by Josh Hutcheson. Every dataset below was opened and checked on 14 October 2026; course details come from each provider’s listing on the same day. See our review methodology.
A good data analytics project answers a business question with a real dataset and shows the steps in between: how you cleaned the data, what you measured, and what you would tell a manager. Below are 15 project ideas, from a first Excel or SQL dashboard to fraud detection and recommendation systems. Every one uses a free public dataset that we opened on 14 October 2026, so you can start today.
The 2022 version of this page listed ten machine learning ideas with no datasets and no advice on presenting the work. Hiring managers in the analytics communities we read keep saying the same thing: a few finished, business-relevant projects that you can talk through in an interview count for more than a long list of half-built notebooks. This version is built around that.
Quick answer
- First project: an online retail sales dashboard from the UCI Online Retail II dataset (about 1.07 million rows) in Excel, SQL or Power BI.
- Best for SQL practice: bike-share trips (Citi Bike) or flight delays (BTS TranStats): large, messy and easy to explain.
- Best business case: telecom customer churn (7,043 customers) or a bank marketing campaign (45,211 calls).
- Best data science step up: credit card fraud detection (492 frauds in 284,807 transactions) or a MovieLens recommender.
- How many? Three to five polished projects that use different tools beat fifteen unfinished ones.
- Want structure? The Google Data Analytics Certificate ends in a guided case study you can put in a portfolio.
What makes a good data analytics project?
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Employers use your projects to answer one question: can this person take a vague request, find the right data and explain the result? A project does that when it has four parts.
- A business question. “Which product categories lost repeat customers in 2011?” works. “Exploring a retail dataset” does not.
- Real, imperfect data. Public datasets with missing values, duplicates and odd codes let you show cleaning, which is most of the job.
- A clear method. The SQL queries, spreadsheet formulas or Python notebook that a reviewer can follow.
- A recommendation. One page or one dashboard that ends with “so we should…”. This is the part most portfolios skip.
One analyst in the r/dataanalysis thread we read said their portfolio showed five major projects across Python, SQL and Power BI or Tableau, but that they had built 15 to 20 smaller ones first to get confident. Others said they were hired with no portfolio at all, because their current job already involved analysis. Treat projects as evidence for the skills you cannot otherwise prove.
A useful trick from the same discussions: read ten job ads for the role you want, list the tools and tasks they repeat, and build your projects around those. A marketing analyst portfolio and a supply chain analyst portfolio should not look the same.
Data analytics projects at a glance
| Project | Level | Tools | Free dataset | What it proves |
|---|---|---|---|---|
| 1. Online retail sales dashboard | Beginner | Excel or SQL, Power BI or Tableau | UCI Online Retail II | Cleaning, aggregation, dashboards |
| 2. Bike-share usage analysis | Beginner | SQL, Tableau | Citi Bike system data | Large-file SQL, time patterns |
| 3. Short-term rental market analysis | Beginner | Excel or Python, maps | Inside Airbnb | Joins, pricing analysis, geography |
| 4. Country development dashboard | Beginner | Tableau or Power BI | World Bank Open Data, Our World in Data | Indicators, time series, storytelling |
| 5. Economic indicator tracker | Beginner | Excel or Python | FRED | Time series, rates of change |
| 6. Customer churn analysis | Intermediate | SQL or Python, Power BI | Telco Customer Churn (Kaggle) | Segmentation, retention metrics |
| 7. Marketing campaign analysis | Intermediate | Python or R | UCI Bank Marketing | Conversion rates, A/B-style comparisons |
| 8. Ecommerce funnel analysis | Intermediate | BigQuery SQL | Google Analytics 4 sample ecommerce data | Event data, funnels |
| 9. Flight delay analysis | Intermediate | SQL, Tableau | BTS TranStats on-time data | Big joins, root-cause analysis |
| 10. Market basket analysis | Intermediate | Python | UCI Online Retail II | Association rules, cross-sell ideas |
| 11. Credit card fraud detection | Advanced | Python (scikit-learn) | Credit Card Fraud Detection (Kaggle) | Imbalanced classification |
| 12. Movie recommendation system | Advanced | Python | MovieLens (GroupLens) | Collaborative filtering |
| 13. Review sentiment analyzer | Advanced | Python (NLP) | IMDB Large Movie Review Dataset | Text classification |
| 14. Fake news detection | Advanced | Python (NLP) | ISOT Fake and Real News (Kaggle) | Text features, model evaluation |
| 15. Weather and climate trend analysis | Advanced | Python or R | NOAA Climate Data Online | Forecasting, seasonality |
Beginner data analytics projects
Start here if you have finished an introductory course in Excel, SQL or Python and have not yet built anything on your own. Each of these can be done in a weekend or two.
1. Online retail sales dashboard
Build a monthly sales dashboard for a UK online gift retailer and answer three questions: which products drive revenue, which countries are growing, and how many customers come back.
Dataset: UCI Online Retail II, every transaction for one UK online retailer between 1 December 2009 and 9 December 2011, about 1,067,371 rows.
What to do:
- Remove canceled orders (invoice numbers starting with “C”) and rows with no customer ID, and note how many you dropped.
- Calculate monthly revenue, average order value and the share of revenue from repeat customers.
- Build a one-page dashboard in Power BI, Tableau Public or Excel with a short written summary.
What it shows an employer: Data cleaning, aggregation and the ability to turn a spreadsheet into a business summary.
2. Bike-share usage analysis
Compare how members and casual riders use a bike-share system: trip length, time of day, day of week and season. End with a recommendation for converting casual riders into members.
Dataset: Citi Bike system data (New York), published as monthly trip files.
What to do:
- Load several months into a database (BigQuery sandbox, PostgreSQL or SQLite) rather than Excel. The files are too large for a spreadsheet.
- Calculate ride length, flag obvious errors (negative or multi-day trips) and compare rider types.
- Map the busiest start stations in Tableau Public.
What it shows an employer: You can work with millions of rows in SQL and explain behavior differences to a non-technical audience.
3. Short-term rental market analysis
Pick a city and answer: what does a typical listing charge, which neighborhoods have the most listings, and how do reviews relate to price?
Dataset: Inside Airbnb, which publishes listings, calendar and review files for cities worldwide.
What to do:
- Join the listings and reviews files on the listing ID.
- Clean the price column (stored as text with a currency symbol) and remove extreme outliers, explaining your rule.
- Show price by neighborhood on a map and write three findings a property investor would care about.
What it shows an employer: Joins, text-to-number cleaning and a real-world framing.
4. Country development dashboard
Choose one theme, such as life expectancy, internet access or CO2 emissions, and build a dashboard comparing countries over time.
Dataset: World Bank Open Data or Our World in Data.
What to do:
- Pick five to ten indicators and download them for every country.
- Reshape the data from wide (one column per year) to long format.
- Highlight two or three countries that changed the most and explain why, citing the source.
What it shows an employer: Reshaping data, time series charts and data storytelling.
5. Economic indicator tracker
Track inflation, unemployment and interest rates for the last 20 years and show how they moved together around recessions.
Dataset: FRED, the Federal Reserve Bank of St. Louis economic database.
What to do:
- Download three or four series and align them to the same monthly dates.
- Calculate year-over-year changes and shade recession periods.
- Write a short note on what the data does and does not show.
What it shows an employer: Working with time series and being careful about cause and effect.
See the Google Data Analytics Certificate capstone →
Intermediate data analytics projects
These need more cleaning, a clearer business question and usually some statistics. They are the projects that read most like real analyst work.
6. Customer churn analysis
A telecom company wants to know which customers are likely to leave and why. Segment customers by contract type, tenure and services, and find the groups with the highest churn rate.
Dataset: Telco Customer Churn on Kaggle: 7,043 customers and 21 columns, with a “Churn” column showing who left in the last month.
What to do:
- Calculate churn rate by contract type, tenure band and payment method.
- Estimate the monthly revenue at risk from the highest-churn segment.
- Recommend one retention action and the metric that would show it worked.
What it shows an employer: Segmentation, retention metrics and turning numbers into a recommendation.
7. Marketing campaign analysis
A Portuguese bank ran phone campaigns selling term deposits. Find which customer groups and call patterns led to sign-ups.
Dataset: UCI Bank Marketing: 45,211 rows in the main file, plus a 41,188-row version ordered by date from May 2008 to November 2010.
What to do:
- Calculate conversion rates by age group, job, month and number of contacts.
- Test whether the differences are larger than chance would explain.
- Advise the marketing team on who to call and when to stop calling.
What it shows an employer: Conversion analysis, basic hypothesis testing and marketing language.
8. Ecommerce funnel analysis
Measure how visitors move from a product view to a purchase on the Google Merchandise Store, and where they drop off.
Dataset: Google’s GA4 sample ecommerce dataset in BigQuery: obfuscated event data from 1 November 2020 to 31 January 2021. The BigQuery sandbox is enough to query it.
What to do:
- Write SQL to count users at each funnel step: view item, add to cart, begin checkout, purchase.
- Compare conversion by device and traffic source.
- Note that the data is obfuscated, so focus on method rather than exact figures.
What it shows an employer: Event-level data and SQL in a cloud warehouse, which many analytics teams use.
9. Flight delay analysis
Find which airlines, airports and times of day have the worst delays, and which delay causes (weather, carrier, air traffic) dominate.
Dataset: BTS TranStats, the U.S. Bureau of Transportation Statistics on-time performance data.
What to do:
- Download one year of flights and load it into SQL.
- Join airport codes to names and calculate average delay and on-time rate.
- Build a dashboard a traveler or an airline operations team could use.
What it shows an employer: Large joins, root-cause thinking and dashboard design.
10. Market basket analysis
Find which products are bought together so a retailer can plan bundles and “frequently bought together” suggestions.
Dataset: The same UCI Online Retail II dataset as project 1, so you can reuse your cleaning work.
What to do:
- Group line items into baskets by invoice number.
- Use the Apriori algorithm (for example the mlxtend library in Python) to find product pairs with high lift.
- Turn the top five rules into concrete cross-sell ideas.
What it shows an employer: An analysis technique beyond descriptive statistics, tied to revenue.
Advanced projects: data science and machine learning
If you are aiming at data scientist roles, or a final-year computing project needs a model, these five use machine learning. They overlap with what many people search for as “data science projects”. Keep the business framing: a model with no explanation of what it is for reads as a tutorial copy.
11. Credit card fraud detection
Build a model that flags fraudulent card transactions and explain the trade-off between catching fraud and blocking real customers.
Dataset: Credit Card Fraud Detection on Kaggle: 492 frauds out of 284,807 transactions (0.172%), with anonymized numeric features.
What to do:
- Explain why accuracy is useless here: predicting “not fraud” every time is 99.8% accurate.
- Compare models using precision, recall and the precision-recall curve.
- Translate the result into missed fraud and false alarms per 10,000 transactions.
What it shows an employer: You understand imbalanced data and choose metrics that match the business cost.
12. Movie recommendation system
Recommend films to a user based on what similar users rated highly.
Dataset: MovieLens from GroupLens. MovieLens 32M has 32 million ratings of 87,585 movies by 200,948 users (released May 2024). The small “latest” file is easier on a laptop.
What to do:
- Start with a simple baseline: recommend the most popular films.
- Build user-based or item-based collaborative filtering and compare it with the baseline.
- Show example recommendations for two different users.
What it shows an employer: Recommender basics and fair comparison against a baseline.
13. Review sentiment analyzer
Classify movie reviews as positive or negative, then apply the model to a handful of product reviews you collect yourself.
Dataset: Large Movie Review Dataset (Stanford): 50,000 labeled IMDB reviews, 25,000 for training and 25,000 for testing.
What to do:
- Start with a bag-of-words model and logistic regression before trying anything heavier.
- Inspect the reviews the model gets wrong and describe the patterns.
- Discuss where the model would fail on other kinds of text.
What it shows an employer: Text cleaning, a working classifier and honest error analysis.
14. Fake news detection
Train a classifier that separates real from fabricated news articles, and test whether it is learning the topic or the writing style.
Dataset: The ISOT Fake and Real News dataset on Kaggle: 23,502 fake and 21,417 true articles.
What to do:
- Remove giveaway text, such as the news agency name at the start of real articles, before training.
- Compare a simple model with a more complex one.
- Write up the limits: a model trained on one period of news will drift.
What it shows an employer: Critical thinking about data leakage, which interviewers often probe.
15. Weather and climate trend analysis
Analyze decades of daily temperature and precipitation for one city and forecast the next season.
Dataset: NOAA’s Climate Data Online (free download after a short request form).
What to do:
- Clean missing days and decide how to handle gaps.
- Decompose the series into trend and seasonality, then fit a simple forecast.
- Compare your forecast with what actually happened in a held-out year.
What it shows an employer: Time series forecasting and validation, not just charts.
What we removed from the 2022 list
The earlier version suggested a stock market prediction app, an AI-powered analytics chatbot and a churn, fake news, movie, fraud, weather, sentiment, flight delay and market basket project, all without datasets. We kept the ideas that still make strong portfolio pieces and paired each with a source. We dropped two:
- Stock market prediction. Daily price prediction rarely beats a naive baseline, and a project that claims it does invites hard interview questions. If you like finance data, project 5 with FRED is the better start.
- AI analytics chatbot. Building on a large language model is a software engineering project. It does not show the analysis skills these roles test.
Data analytics project ideas for final-year students
If you need a final-year or capstone project, the same ideas work, but a university project needs more structure than a portfolio piece. Most marking schemes reward a clear question, a literature or background section, a documented method, evaluation and a discussion of limits.
| Week | What to deliver |
|---|---|
| 1 to 2 | Pick the question and dataset; confirm with your supervisor that the data license allows academic use. |
| 3 to 4 | Background reading: two or three papers or industry reports on the problem. |
| 5 to 8 | Cleaning and exploratory analysis; document every decision. |
| 9 to 11 | Model or main analysis, plus evaluation against a simple baseline. |
| 12 to 14 | Write-up, dashboard or demo, and a limitations section. |
Projects 6, 7, 11 and 12 suit a computing or business analytics degree well because they combine a business question with measurable evaluation. If your course is more statistical, project 15 gives you time series methods to discuss.
How to present a project in your portfolio
A reviewer gives each project a minute or two. Make that minute count.
- Lead with the answer. The first line of your README or case study should be the finding and the recommendation.
- One page per project. Question, data, method, result, recommendation, limitations. Link the code underneath.
- Show the dashboard. Publish to Tableau Public or share a Power BI report or screenshots, so nobody has to run your code.
- Use GitHub properly. A clear folder structure, a requirements file and a notebook that runs from top to bottom.
- Be ready to talk. Several analysts we read said being able to explain a project in an interview mattered more than the certificate that came with it.
Where to host it: GitHub for code, Tableau Public for dashboards, Kaggle notebooks for anything built on a Kaggle dataset, and a simple personal site or LinkedIn featured section that links them together.
Tools you need (most are free)
| Tool | Use it for | Cost |
|---|---|---|
| Excel or Google Sheets | Cleaning small files, pivot tables, quick charts | Sheets is free |
| SQL (BigQuery sandbox, PostgreSQL, SQLite) | Querying large files, joins, aggregations | Free |
| Python with pandas | Cleaning, analysis, modeling (pandas is at version 3.0 in 2026) | Free |
| Tableau Public | Publishing interactive dashboards | Free |
| Power BI Desktop | Dashboards on Windows | Free to build; sharing needs a paid license |
| Jupyter or Kaggle notebooks | Documented analysis you can share | Free |
If you are still deciding between tools, see our guides to SQL courses, Tableau courses and data science tools.
Courses that include guided projects
If you would rather follow a structure first, these programs end with portfolio projects. We checked each listing on 14 October 2026.
| Program | Length | Rating and size | Price | Projects |
|---|---|---|---|---|
| Google Data Analytics Professional Certificate (Coursera) | 9 courses; about 6 months at 10 hours a week | 4.8 from 182,625 reviews; 3,878,612 enrolled | Coursera subscription | Ends with a case study capstone |
| IBM Data Analyst Professional Certificate (Coursera) | 11 courses; about 4 months at 10 hours a week | 4.6 from 99,762 reviews; 589,980 enrolled | Coursera subscription | Hands-on labs and a capstone |
| DataCamp data analyst tracks and certification | Self-paced | Interactive exercises | $14 a month billed annually (Premium) | Guided projects inside the platform |
| Udacity Data Analyst Nanodegree | 43 hours | 3 projects with reviewer feedback | $249 a month or $846 for 4 months | Mentor-reviewed projects |
Our view: the Google certificate is the best-structured start for a complete beginner, because its capstone is effectively project 2 above with guidance. IBM’s goes deeper on Python and SQL. DataCamp suits people who learn by typing code every day. Udacity costs more but is the only one here where a person reviews your project and sends it back with comments. See our Google Data Analytics Certificate review and Udacity Data Analyst Nanodegree review for the details.
Start the Google Data Analytics Certificate →
Other options: the IBM Data Analyst Professional Certificate, DataCamp’s Data Analyst in Python track and the Udacity Data Analyst Nanodegree.
Mistakes that make projects look weak
- Using the Titanic or Iris dataset as your main project. Fine for practice; every reviewer has seen it hundreds of times.
- No question. Charts without a point read as “I followed a tutorial”.
- Hiding the cleaning. Explain what you removed and why. It is the most job-like part of the work.
- Overclaiming a model. 99% accuracy on imbalanced data is a red flag, not a result (see project 11).
- Only one tool. Spread your projects across SQL, a spreadsheet or BI tool, and Python or R.
- Unfinished repositories. Three finished projects beat ten abandoned notebooks.
Dataset sizes, course ratings and prices come from the official pages linked in this article, read on 14 October 2026. They change every cycle, so confirm on the linked page before you apply or pay.
Frequently asked questions
What are good data analytics projects for beginners?
Start with a sales dashboard from the UCI Online Retail II dataset, a bike-share usage analysis from Citi Bike data, or a country comparison from World Bank Open Data. Each uses free data, needs only Excel or SQL plus a dashboard tool, and can be finished in a weekend or two.
How many projects should a data analyst portfolio have?
Three to five finished projects is enough for most entry-level roles. Use different tools across them (SQL, a BI tool such as Tableau or Power BI, and Python or R) and make each one answer a business question.
Where can I find free datasets for data analytics projects?
Kaggle, the UCI Machine Learning Repository, Data.gov, World Bank Open Data, Our World in Data, FRED, Google BigQuery public datasets, and city open-data portals all publish free datasets. Check each dataset license before using it in a published project.
What is the difference between a data analytics project and a data science project?
A data analytics project usually describes and explains what happened, using SQL, spreadsheets and dashboards. A data science project usually predicts or classifies something with a statistical or machine learning model. Many portfolios include both.
Do guided course projects count in a portfolio?
They count, but reviewers recognize them. Use a guided capstone such as the Google Data Analytics case study as a starting point, then add your own question or a second dataset so the project shows your thinking.
Can I use a data analytics project for a final-year university project?
Yes. Projects such as churn analysis, marketing campaign analysis, fraud detection or a recommendation system work well, as long as you add a background section, a documented method, evaluation against a baseline and a limitations section.
Should I put my projects on GitHub?
Yes for code and notebooks. Add a short README that states the question, data, method and result. Publish dashboards to Tableau Public or share screenshots so reviewers do not need to run anything.
Which tools should I learn before starting a project?
SQL and one spreadsheet or BI tool are enough for beginner projects. Add Python with pandas for intermediate projects and scikit-learn for the machine learning projects.
Practice with DataCamp’s Data Analyst in Python track →
Related: If a job ad asks for Alteryx, several of these projects work well as Alteryx workflows. Our guide to Alteryx courses lists the fastest ways to learn it, including free options.