Pandas is a powerful Python library that is commonly used by data scientists and data analysts to explore, clean, and process their data. Here are five common methods that you can expect to use when working with Pandas. 

What do you do when you can’t find a dataset for your project? Well, one option is to scrape websites for the data you need. Here’s an example of how it’s done.

Google Sheets makes it easy to clean data. Let’s explore three Google Sheets data cleaning features — Cleanup Suggestions, Remove Duplicates and Trim Whitespace.

Google Sheets Import Functions provide a hassle-free way for users to import data from a wide range of online sources, including other Google Sheets, with ease.

Looking for data for your next project? Check out Open Data sources like Statistics Canada, Data.gov, and the CDC for datasets. They’re free to access, reuse, and distribute. Check out the list we’ve assembled.

With a multitude of chart types available, it's essential to match the data's characteristics and the story you want to tell with the appropriate visualization.

Datawrapper is a popular data visualization tool trusted by journalists, educational institutions, finance companies, government institutions, and think tanks. It’s a useful application for those seeking to build interactive, responsive charts, tables, and maps, but have limited coding skills or time.

We spend a lot of time working in Google Sheets and we are pleasantly surprised when we learn something new that improves our workflow. It’s pretty unbelievable that we hadn’t come across this before. One thing we recently discovered is the ImportHTML function.

Books on data science, data analytics, and data visualization are indispensable companions for anyone looking to harness the power of data to make informed decisions in an increasingly data-driven world.

From open government sites to subscription services and everything in between – use this as your go-to reference point when searching for data for your next project.