Beginner · Sales · Retail
Superstore Sales
A great starting point for sales, profit and regional analysis.
Source: Kaggle
View Original Dataset →
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BLOOMINDATA · DATASET LIBRARY
My Dataset Library
Datasets I'm exploring and collecting as I learn data.
When I started learning data analytics, I realized that finding the right dataset can sometimes be harder than analyzing it.
So I started collecting datasets that are actually useful for practice — from simple Excel exercises to SQL projects, Power BI dashboards, Python analysis and machine learning.
This is my growing collection of datasets for learning by doing.
Last updated September 2026 — new datasets added as I find them.
How This Is Organized
Grouped by difficulty, domain and skills — so you spend less time searching and more time working with data.
Find Your Dataset
Looking for something specific? Search by dataset name, domain, skill or keyword.
Browse by Level
Browse by Tool
Browse by Domain
Showing all datasets
Where to Start
Start with something you can understand. Then gradually move toward messier, larger and more challenging datasets.
START HERE
Absolute Beginner
Superstore
Student Performance
Online Retail
Best for: Excel · SQL · Power BI
BUILD CONFIDENCE
Beginner → Intermediate
Bank Marketing
Telco Churn
Olist E-commerce
Best for: SQL · Python · Power BI
GO DEEPER
Intermediate
NYC Taxi
Air Quality
Energy
Supply Chain
Best for: Python · Statistics · Time Series
EXPLORE DATA SCIENCE
Advanced
Fraud Detection
M5 Forecasting
Fashion-MNIST
IMDb Reviews
Best for: ML · NLP · Deep Learning
01 · START HERE
Absolute Beginner
These are the datasets I'd recommend when you're still getting comfortable with rows, columns, formulas, SQL queries and dashboards.
Beginner → Intermediate · Retail · E-commerce
UCI Online Retail II
Real-world transactions for customer and sales analysis beyond basic dashboards.
Source: UCI Machine Learning Repository · License: CC BY 4.0
View Original Dataset →Beginner · Education · Statistics
Student Performance
Explore student performance and what factors influence it.
Source: UCI Machine Learning Repository · License: CC BY 4.0
View Original Dataset →Beginner · HR · People Analytics
IBM HR Analytics — Employee Attrition
Employee demographics, roles, compensation and attrition.
Source: Kaggle
View Original Dataset →Beginner → Intermediate · Real Estate
Ames Housing / House Prices
Explore housing features, pricing and regression concepts.
Source: Kaggle
View Original Dataset →Beginner → Intermediate · Entertainment
TMDB 5000 Movie Dataset
Explore movie metadata, genres, cast, popularity, budgets and revenue.
Source: Kaggle
View Original Dataset →02 · BUILD CONFIDENCE
Beginner → Intermediate
Once basic analysis starts feeling comfortable, these datasets are a good next step. They introduce more variables, relational data, business problems and more interesting analytical questions.
Intermediate · E-commerce · Business Analytics
Olist Brazilian E-Commerce
Multi-table e-commerce data: orders, customers, products, payments and reviews.
Source: Kaggle · License: CC BY-NC-SA 4.0
View Original Dataset →Beginner → Intermediate · Telecom · Customer Analytics
Telco Customer Churn
Explore customer contracts, services, charges and churn behavior.
Source: Kaggle
View Original Dataset →Beginner → Intermediate · Marketing
Customer Personality Analysis
Explore customer demographics, purchasing behavior and campaign responses.
Source: Kaggle
View Original Dataset →Beginner → Intermediate · Banking · Marketing
Bank Marketing
Marketing campaign data — explore what drives term deposit subscriptions.
Source: UCI Machine Learning Repository · License: CC BY 4.0
View Original Dataset →Intermediate · Environment · Time Series
Beijing Multi-Site Air Quality
Hourly air-quality data from multiple monitoring sites — a time-series intro.
Source: UCI Machine Learning Repository · License: CC BY 4.0
View Original Dataset →Intermediate · Healthcare · Predictive Analytics
Diabetes 130-US Hospitals
Hospital encounter data for a challenging healthcare analytics problem.
Source: UCI Machine Learning Repository · License: CC BY 4.0
View Original Dataset →03 · GO DEEPER
Intermediate Datasets
This is where I'd start moving beyond simple dashboards. These datasets can help you practice deeper analysis, time series, fraud detection, geospatial data and predictive modelling.
04 · BEYOND ANALYTICS
Data Science & Machine Learning
Data analytics was my starting point, but data doesn't stop at dashboards. These datasets are here for exploring machine learning, NLP, computer vision, forecasting and predictive analytics.
Advanced · Retail · Forecasting
M5 Forecasting
A large-scale retail forecasting problem for exploring time-series modelling.
Source: Kaggle
View Original Dataset →Advanced · NLP · Sentiment Analysis
IMDb 50K Movie Reviews
A classic dataset for sentiment classification and NLP practice.
Source: Kaggle
View Original Dataset →Advanced · Computer Vision
Fashion-MNIST
A benchmark dataset for image classification and deep learning.
Source: Zalando Research
View Original Dataset →Advanced · NLP · Text Classification
AG News
A news-topic dataset for NLP and text classification practice.
Source: Hugging Face
View Original Dataset →WHAT I'M PRACTICING
Different Datasets. Different Skills.
Not every dataset needs the same approach. Some are perfect for spreadsheets. Others make much more sense with SQL, Python or machine learning.
Click a skill to filter datasets that use it ↑
MY APPROACH
My First Questions When I Open a Dataset
I've learned that it's tempting to open a dataset and immediately start making charts. I'm trying to slow down and ask better questions first.
1
What does one row actually represent?
2
What does each column mean?
3
What's missing, inconsistent or unusual?
4
What patterns can I find?
5
What questions can this data answer?
6
What questions can it NOT answer?
7
Can I turn what I found into a useful story?
Because good analysis starts with good questions.
My Data Learning Workflow
STEP 01
Find
Choose a dataset that interests me.
STEP 02
Understand
Understand what each row and column represents.
STEP 03
Clean
Look for missing values, duplicates, incorrect formats and inconsistencies.
STEP 04
Explore
Find patterns, trends, relationships and outliers.
STEP 05
Ask
Turn observations into meaningful questions.
STEP 06
Analyze
Use the right analytical method or tool.
STEP 07
Visualize
Turn findings into clear visuals.
STEP 08
Tell the Story
Explain what the data actually means.
I'm trying to follow this process instead of jumping straight into making pretty dashboards.
DON'T JUST DOWNLOAD
Turn a Dataset Into a Project
A dataset becomes much more useful when you actually do something with it. Here are some directions I'd explore:
| Dataset | Project Idea |
|---|---|
| Superstore | Sales & Profit Dashboard |
| Online Retail II | Customer Segmentation |
| Bank Marketing | Campaign Analysis |
| Telco Churn | Customer Churn Dashboard |
| NYC Taxi | Taxi & Transportation Analysis |
| Energy Consumption | Consumption Trend Analysis |
| Credit Card Fraud | Fraud Classification |
| IMDb Reviews | Sentiment Analysis |
| Fashion-MNIST | Image Classification |
| M5 Forecasting | Demand Forecasting |
Don't aim for the perfect project. Start with one question.
A NOTE FROM ME
I'm Learning Too.
I'm not building this library because I have every dataset figured out.
I'm building it because I'm learning too.
Every dataset gives me another opportunity to practice asking better questions, finding patterns, making mistakes and understanding what the numbers are actually saying.
If you're learning data analytics too, I hope this library saves you some searching — and gives you something interesting to work with.
Let's learn by doing.
— Priyanka Lakra
Building BloomInData, one dataset at a time.
Explore the Original Sources
The datasets in this library come from established repositories, public data sources and dataset platforms. BloomInData helps you discover and organize them in one place — the original dataset remains with its respective source.
How This Library Works
01
Browse
Find a dataset by level, skill or domain.
02
Choose
Pick something that matches where you are in your learning journey.
03
Explore
Read the dataset description and understand what you can practice.
04
Go to Source
Open the original repository and access the dataset there.
05
Build
Use it to practice, analyze and create something of your own.
Questions I Get Asked
A few things people ask me about this library.
Are these datasets really free?
Yes. Every dataset I've listed here is free to access from its original source — Kaggle, UCI, NYC TLC, Hugging Face or Zalando. A few platforms (like Kaggle) ask you to create a free account before downloading, but there's no paywall on the data itself.
Do you host the datasets yourself?
No. This library only links out to the original source. I'm collecting and organizing datasets I find useful as I learn — I don't copy, re-host or claim ownership of any of them.
Can I use these for commercial projects?
It depends on the individual dataset's license. Some are CC BY 4.0 (fairly permissive), others have more restrictive terms like CC BY-NC-SA. Always check the license on the original dataset page before using it commercially — I've noted the license where I know it, but the source is the final word.
Which dataset should I start with if I'm a complete beginner?
Superstore Sales is where I'd point most people first — it's simple, well-documented, and works great in Excel, SQL or Power BI. Student Performance and Online Retail II are good next steps.
Is this list updated regularly?
Yes — I'm adding datasets as I find ones that are genuinely useful for practice, not just dumping everything I come across. Quality over quantity.
Can I suggest a dataset for the list?
Definitely — if you've found a dataset you think belongs here, or you're looking for something specific that isn't listed, reach out through the Get in Touch page and I'll take a look.
Your Next Data Project Could Start Here.
You don't need the perfect dataset. Pick one. Open it. Ask a question. Start exploring.
Your next data project might be hiding in a CSV.
Dataset Sources & Licensing
BloomInData is a personal collection of datasets I'm exploring and learning from.
The datasets listed here are owned and maintained by their respective authors, institutions, repositories or data providers. Licensing and usage conditions vary by dataset.
Always review the original dataset page and applicable license before downloading, redistributing or using a dataset commercially.
BloomInData does not claim ownership of third-party datasets.