Google Books Ngram : Download the raw data from the Google Books Ngram to explore phrase trends in books published from 1960 to 2015.ĩ.
Latest Netflix Data : This Kaggle dataset (updated in April 2021) includes movie data broken down into 26 attributes.Ĩ. World Health Organization COVID-19 Dashboard : Track the latest coronavirus numbers by country or WHO region.ħ.
FBI Crime Data Explorer (CDE) : Explore crime data collected by more than 18,000 law enforcement agencies.Ħ. US Census : Learn more about the people and economy of the United States with the latest census data from 2020.ĥ. NASA : If you’re interested in space and earth science, see what you can find among the tens of thousands of public datasets made available by NASA.Ĥ. World Happiness Report 2021 : What makes the world’s happiest countries so happy?ģ. National Centers for Environmental Information : Dig into the world’s largest provider of weather and climate data.Ģ.
Here are 10 fun and free datasets to get you started in your explorations.ġ. Think about what problems you could potentially solve with the data.Įxample exploratory data analysis project: This data analyst took an existing dataset on American universities in 2013 from Kaggle and used it to explore what makes students prefer one university over another.Īn EDA project is an excellent time to take advantage of the wealth of public datasets available online. Test hypotheses and validate assumptions about the data. Look for trends, patterns, and anomalies in the data. Either way, you’ll want to accomplish the following during these early investigations.ĭiscover the underlying structure of the data. This could be done separate from or in conjunction with data cleaning. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. Exploratory data analysis (EDA)ĭata analysis is all about answering questions with data. Some sites where you can find “dirty” data sets to work with include:Įxample data cleaning project: This Medium article outlines how data analyst Raahim Khan cleaned a set of daily-updated statistics on trending YouTube videos. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent.Īs you look for a data set to practice cleaning, look for one that includes multiple files gathered from multiple sources without much curation. Data cleaningĪ significant part of your role as a data analyst is cleaning data to make it ready to analyze. Schneider of Wedding Crunchers scraped some 60,000 New York Times wedding announcements published from 1981 to 2016 to measure the frequency of specific phrases. If you’re unsure where to start, here are some websites with interesting data options to inspire your project:Įxample web scraping project: Todd W. You’ll also find several tools that automate the process (many offer a free trial), like Octoparse or ParseHub.
If you don’t know how to code, don’t worry. If you know some Python, you can use tools like Beautiful Soup or Scrapy to crawl the web for interesting data. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not they’ve already been compiled. While you’ll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you’re able to find and scrape your own data as well. These data analytics project ideas reflect the tasks often fundamental to many data analyst roles.
As an aspiring data analyst, you’ll want to demonstrate a few key skills in your portfolio.