Top 3 Datasets for Data Cleaning Projects | Datasets for Datscience

What are Datasets?

A data set is a collection of related, discrete items of related data that may be accessed individually or in combination or managed as a whole entity. A data set is organized into some type of data structure. In a database, for example, a data set might contain a collection of business data (names, salaries, contact information, sales figures, and so forth). The database itself can be considered a data set, as can bodies of data within it related to a particular type of information, such as sales data for a particular corporate department.

The term data set originated with IBM, where its meaning was similar to that of file.

Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms such as deep learning, computer hardware, and, less-intuitively, the availability of high-quality training datasets.

If you’ve ever worked on a personal data science project, you’ve probably spent a lot of time browsing the internet looking for interesting data sets to analyze. It can be fun to sift through dozens of data sets to find the perfect one. But it can also be frustrating to download and import several csv files, only to realize that the data isn’t that interesting after all. Luckily, there are online repositories that curate data sets and (mostly) remove the uninteresting ones.


In this post, let’s look at the sites to find Datasets for Data Cleaning Projects

Data Sets for Data Cleaning Projects

Sometimes, it can be very satisfying to take a data set spread across multiple files, clean it up, condense it all into a single file, and then do some analysis. In data cleaning projects, it can take hours of research to figure out what each column in the data set means. It may turn out that the data set you’re analyzing isn’t really suitable for what you’re trying to do, and you’ll need to start over.

That can be frustrating, but it’s a common part of every data science job, and it requires practice.

When looking for a good data set for a data cleaning project, you want it to:

  • Be spread over multiple files.
  • Have a lot of nuance, and many possible angles to take.
  • Require a good amount of research to understand.
  • Be as “real-world” as possible.

These types of data sets are typically found on websites that collect and aggregate data sets. These aggregators tend to have data sets from multiple sources, without much curation. In this case, that’s a good thing — too much curation gives us overly neat data sets that are hard to do extensive cleaning on.

1. is a user-driven data collection site (among other things) where you can search for, copy, analyze, and download data sets. You can also upload your own data to and use it to collaborate with others.

The site includes some key tools that make working with data from the browser easier. You can write SQL queries within the site interface to explore data and join multiple data sets. They also have SDKs for R and Python that make it easier to acquire and work with data in your tool of choice.

All of the data is accessible from the main site, but you’ll need to create an account, log in, and then search for the data you’d like.

Here are some examples:

2. an aggregator of public data sets from a variety of US government agencies, as part of a broader push towards more open government. Data can range from government budgets to school performance scores. Much of the data requires additional research, and it can sometimes be hard to figure out which data set is the “correct” version. Anyone can download the data, although some data sets will ask you to jump through additional hoops, like agreeing to licensing agreements before downloading.

You can browse the data sets on directly, without registering. You can browse by topic area, or search for a specific data set.

Here are some examples:

3. The World Bank

The World Bank is a global development organization that offers loans and advice to developing countries. The World Bank regularly funds programs in developing countries, then gathers data to monitor the success of these programs.

You can browse world bank data sets directly, without registering. The data sets have many missing values (which is great for cleaning practice), and it sometimes takes several clicks to actually get to data.

Here are some examples:


4. /r/datasets

Reddit, a popular community discussion site, has a section devoted to sharing interesting data sets. It’s called the datasets subreddit, or /r/datasets. The scope and quality of these data sets varies a lot, since they’re all user-submitted, but they are often very interesting and nuanced.

You can browse the subreddit here without an account (although a free account will be required to comment or submit data sets yourself). You can also see the most highly-upvoted data sets of all time here.

Here are some examples:


5. Academic Torrents

Academic Torrents is data aggregator geared toward sharing the data sets from scientific papers. It has all sorts of interesting (and often massive) data sets, although it can sometimes be difficult to get context on a particular data set without reading the original paper and/or having some expertise in the relevant domains of science.

You can browse the data sets directly on the site. Since it’s a torrent site, all of the data sets can be immediately downloaded, but you’ll need a Bittorrent client. Deluge is a good free option that’s available for Windows, Mac, and Linux.

Here are some examples:

  • Enron Emails — a set of many emails from executives at Enron, a company that famously went bankrupt.
  • Student Learning Factors — a set of factors that measure and influence student learning.
  • News Articles — contains news article attributes and a target variable.



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